{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":19866,"offset":19856,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u0412\u043e\u0442 \u043f\u043e\u043b\u043d\u044b\u0439 \u043e\u0431\u043d\u043e\u0432\u043b\u0451\u043d\u043d\u044b\u0439 \u043a\u043e\u0434 \u0441\u0440\u0435\u0434\u044b `StockTradingEnv` \u0441 \u0443\u0447\u0451\u0442\u043e\u043c \u0432\u0441\u0435\u0445 \u0432\u0430\u0448\u0438\u0445 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0439:\n\n---\n\n### **\u0424\u0438\u043d\u0430\u043b\u044c\u043d\u0430\u044f \u0432\u0435\u0440\u0441\u0438\u044f `StockTradingEnv`**\n\n```python\nimport gymnasium as gym\nimport numpy as np\nfrom gymnasium import spaces\n\nclass StockTradingEnv(gym.Env):\n    \"\"\"\u0421\u0440\u0435\u0434\u0430 \u0434\u043b\u044f RL-\u0442\u043e\u0440\u0433\u043e\u0432\u043b\u0438 \u0430\u043a\u0446\u0438\u044f\u043c\u0438.\"\"\"\n\n    def __init__(self, data, initial_balance=1000, window_size=10, max_shares=1):\n        \"\"\"\n        Args:\n            data (DataFrame): \u0420\u044b\u043d\u043e\u0447\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0441 \u043a\u043e\u043b\u043e\u043d\u043a\u0430\u043c\u0438 ['Open', 'High', 'Low', 'Close'].\n            initial_balance (float): \u041d\u0430\u0447\u0430\u043b\u044c\u043d\u044b\u0439 \u0431\u0430\u043b\u0430\u043d\u0441 \u0430\u0433\u0435\u043d\u0442\u0430.\n            window_size (int): \u0420\u0430\u0437\u043c\u0435\u0440 \u0441\u043a\u043e\u043b\u044c\u0437\u044f\u0449\u0435\u0433\u043e \u043e\u043a\u043d\u0430 \u0434\u043b\u044f \u043d\u0430\u0431\u043b\u044e\u0434\u0435\u043d\u0438\u0439.\n            max_shares (int): \u041c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0430\u043a\u0446\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0430\u0433\u0435\u043d\u0442 \u043c\u043e\u0436\u0435\u0442 \u043a\u0443\u043f\u0438\u0442\u044c \u0438 \u0434\u0435\u0440\u0436\u0430\u0442\u044c \u043e\u0434\u043d\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e.\n        \"\"\"\n        super(StockTradingEnv, self).__init__()\n        \n        # \u041b\u0438\u043c\u0438\u0442 \u043d\u0430 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0430\u043a\u0446\u0438\u0439\n        self.max_shares = max_shares\n\n        # \u0420\u044b\u043d\u043e\u0447\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435\n        assert \"Close\" in data.columns, \"\u0414\u0430\u043d\u043d\u044b\u0435 \u0434\u043e\u043b\u0436\u043d\u044b \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0442\u044c \u043a\u043e\u043b\u043e\u043d\u043a\u0443 'Close'!\"\n        self.data = data.reset_index(drop=True)  # \u0421\u0431\u0440\u0430\u0441\u044b\u0432\u0430\u0435\u043c \u0438\u043d\u0434\u0435\u043a\u0441\u044b \u0434\u043b\u044f \u0443\u0434\u043e\u0431\u0441\u0442\u0432\u0430 \u0434\u043e\u0441\u0442\u0443\u043f\u0430\n        self.n_steps = len(self.data)\n\n        # \u0422\u0435\u043a\u0443\u0449\u0438\u0439 \u0438\u043d\u0434\u0435\u043a\u0441 \u0448\u0430\u0433\u0430\n        self.current_step = 0\n        self.window_size = window_size\n\n        # \u0424\u0438\u043d\u0430\u043d\u0441\u043e\u0432\u044b\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u0438\n        self.initial_balance = initial_balance\n        self.balance = initial_balance\n        self.shares = 0\n        self.net_worth = initial_balance  # \u0427\u0438\u0441\u0442\u0430\u044f \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044f\n\n        # Action Space: 0 = Hold, 1 = Buy, 2 = Sell\n        self.action_space = spaces.Discrete(3)\n\n        # Observation Space: \u0440\u044b\u043d\u043e\u0447\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 + \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044c (6 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 \u043d\u0430 \u043a\u0430\u0436\u0434\u0443\u044e \u0441\u0432\u0435\u0447\u0443)\n        self.observation_space = spaces.Box(\n            low=-np.inf, high=np.inf, shape=(window_size, 6), dtype=np.float32\n        )\n\n    def reset(self):\n        \"\"\"\u0421\u0431\u0440\u043e\u0441 \u0441\u0440\u0435\u0434\u044b \u0432 \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0435 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u0435.\"\"\"\n        self.current_step = self.window_size  # \u041d\u0430\u0447\u0438\u043d\u0430\u0435\u043c \u0441 \u0448\u0430\u0433\u0430 \u043f\u043e\u0441\u043b\u0435 \u043e\u043a\u043d\u0430\n        self.balance = self.initial_balance\n        self.shares = 0\n        self.net_worth = self.initial_balance\n\n        return self._next_observation()\n\n    def _next_observation(self):\n        \"\"\"\u0413\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0442\u0435\u043a\u0443\u0449\u0435\u0433\u043e \u043d\u0430\u0431\u043b\u044e\u0434\u0435\u043d\u0438\u044f (\u0438\u0441\u0442\u043e\u0440\u0438\u044f + \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044c).\"\"\"\n        end = self.current_step\n        start = max(0, end - self.window_size)\n\n        # \u0420\u044b\u043d\u043e\u0447\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 (\u0441\u043a\u043e\u043b\u044c\u0437\u044f\u0449\u0435\u0435 \u043e\u043a\u043d\u043e)\n        market_data = self.data.iloc[start:end][['Open', 'High', 'Low', 'Close']].to_numpy()\n\n        # \u0424\u0438\u043d\u0430\u043d\u0441\u043e\u0432\u044b\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438 (\u043a\u043e\u043f\u0438\u0440\u0443\u0435\u043c \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0439 \u0441\u0442\u0440\u043e\u043a\u0438 \u043e\u043a\u043d\u0430)\n        portfolio = np.array([self.balance, self.net_worth] * self.window_size).reshape(self.window_size, 2)\n\n        # \u041e\u0431\u044a\u0435\u0434\u0438\u043d\u044f\u0435\u043c \u0440\u044b\u043d\u043e\u0447\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0438 \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044c\n        observation = np.hstack([market_data, portfolio])\n        return observation\n\n    def step(self, action):\n        \"\"\"\u0412\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u0438 \u043f\u0435\u0440\u0435\u0445\u043e\u0434 \u0441\u0440\u0435\u0434\u044b \u043d\u0430 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0448\u0430\u0433.\"\"\"\n        self.current_step += 1\n\n        # \u0422\u0435\u043a\u0443\u0449\u0430\u044f \u0438 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0430\u044f \u0446\u0435\u043d\u044b \u0437\u0430\u043a\u0440\u044b\u0442\u0438\u044f\n        current_price = self.data.iloc[self.current_step][\"Close\"]\n        previous_price = self.data.iloc[self.current_step - 1][\"Close\"]\n\n        # \u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043d\u0430\u0433\u0440\u0430\u0434\n        reward = 1  # \u041d\u0435\u0439\u0442\u0440\u0430\u043b\u044c\u043d\u0430\u044f \u043d\u0430\u0433\u0440\u0430\u0434\u0430\n        price_reward = 1\n        trade_reward = 1\n\n        # \u041e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439 \u0430\u0433\u0435\u043d\u0442\u0430\n        if action == 1:  # Buy\n            if self.balance >= current_price and self.shares < self.max_shares:  # \u0423\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u043c \u043b\u0438\u043c\u0438\u0442 \u0430\u043a\u0446\u0438\u0439\n                self.balance -= current_price\n                self.shares += 1\n        elif action == 2:  # Sell\n            if self.shares > 0:  # \u0415\u0441\u043b\u0438 \u0435\u0441\u0442\u044c \u0430\u043a\u0446\u0438\u0438 \u0434\u043b\u044f \u043f\u0440\u043e\u0434\u0430\u0436\u0438\n                total_sold_value = self.shares * current_price\n                self.balance += total_sold_value\n                self.shares = 0\n\n                # \u041d\u0430\u0433\u0440\u0430\u0434\u0430 \u0437\u0430 \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u0435 \u0441\u0434\u0435\u043b\u043a\u0438\n                trade_reward = self.balance / self.initial_balance\n                reward *= trade_reward\n\n        # \u041e\u0431\u043d\u043e\u0432\u043b\u044f\u0435\u043c \u0447\u0438\u0441\u0442\u0443\u044e \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044f\n        self.net_worth = self.balance + (self.shares * current_price)\n\n        # \u041d\u0430\u0433\u0440\u0430\u0434\u0430 \u0437\u0430 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0446\u0435\u043d\u044b \u0430\u043a\u0446\u0438\u0439 (\u0441\u0442\u0438\u043c\u0443\u043b \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u0442\u044c \u0442\u0440\u0435\u043d\u0434)\n        if self.shares > 0:  # \u0415\u0441\u043b\u0438 \u0443\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u044e\u0442\u0441\u044f \u0430\u043a\u0446\u0438\u0438\n            price_reward = current_price / previous_price  # \u0415\u0441\u043b\u0438 \u0446\u0435\u043d\u0430 \u0440\u0430\u0441\u0442\u0451\u0442, reward > 1\n            reward *= price_reward\n\n        # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u044f \u044d\u043f\u0438\u0437\u043e\u0434\u0430\n        done = self.current_step >= self.n_steps - 1\n\n        # \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044f \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 (info)\n        info = {\n            \"step\": self.current_step,  # \u0422\u0435\u043a\u0443\u0449\u0438\u0439 \u0448\u0430\u0433\n            \"action\": action,  # \u0414\u0435\u0439\u0441\u0442\u0432\u0438\u0435 \u0430\u0433\u0435\u043d\u0442\u0430\n            \"current_price\": current_price,  # \u0422\u0435\u043a\u0443\u0449\u0430\u044f \u0446\u0435\u043d\u0430 \u0437\u0430\u043a\u0440\u044b\u0442\u0438\u044f\n            \"previous_price\": previous_price,  # \u041f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0430\u044f \u0446\u0435\u043d\u0430 \u0437\u0430\u043a\u0440\u044b\u0442\u0438\u044f\n            \"price_reward\": price_reward,  # \u041d\u0430\u0433\u0440\u0430\u0434\u0430 \u0437\u0430 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0446\u0435\u043d\u044b\n            \"trade_reward\": trade_reward,  # \u041d\u0430\u0433\u0440\u0430\u0434\u0430 \u0437\u0430 \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u0435 \u0441\u0434\u0435\u043b\u043a\u0438\n            \"total_reward\": reward,  # \u0418\u0442\u043e\u0433\u043e\u0432\u0430\u044f \u043d\u0430\u0433\u0440\u0430\u0434\u0430\n            \"balance\": self.balance,  # \u0414\u043e\u0441\u0442\u0443\u043f\u043d\u044b\u0435 \u0434\u0435\u043d\u044c\u0433\u0438\n            \"shares\": self.shares,  # \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0430\u043a\u0446\u0438\u0439\n            \"net_worth\": self.net_worth,  # \u0427\u0438\u0441\u0442\u0430\u044f \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044f\n        }\n\n        return self._next_observation(), reward, done, info\n\n    def render(self):\n        \"\"\"\u0412\u044b\u0432\u043e\u0434 \u0442\u0435\u043a\u0443\u0449\u0435\u0433\u043e \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u0434\u043b\u044f \u043e\u0442\u043b\u0430\u0434\u043a\u0438/\u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438.\"\"\"\n        print(f\"Step: {self.current_step}, Balance: {self.balance:.2f}, Shares: {self.shares}, Net Worth: {self.net_worth:.2f}\")\n```\n\n---\n\n### **\u0427\u0442\u043e \u043d\u043e\u0432\u043e\u0433\u043e \u0432 \u044d\u0442\u043e\u043c \u043a\u043e\u0434\u0435:**\n1. **\u041e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0438\u0435 \u043d\u0430 \u043f\u043e\u043a\u0443\u043f\u043a\u0438 (`max_shares`):**\n   - \u0422\u0435\u043f\u0435\u0440\u044c \u0430\u0433\u0435\u043d\u0442 \u043d\u0435 \u043c\u043e\u0436\u0435\u0442 \u043a\u0443\u043f\u0438\u0442\u044c \u0431\u043e\u043b\u0435\u0435 `max_shares` \u0430\u043a\u0446\u0438\u0439. \u0412\u044b \u0437\u0430\u0434\u0430\u0451\u0442\u0435 \u044d\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u043e\u043c \u043f\u0440\u0438 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u0438 \u0441\u0440\u0435\u0434\u044b (\u043f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e `max_shares=1`).\n   \n2. **\u0420\u0430\u0441\u0447\u0451\u0442 `reward`:**\n   - \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f **\u043e\u0442\u043d\u043e\u0448\u0435\u043d\u0438\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439** \u0434\u043b\u044f \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 (`reward = price_reward * trade_reward`).\n   - \u0423\u0447\u0451\u0442 \"\u0442\u0440\u0435\u043d\u0434\u0430\" \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u044f \u0446\u0435\u043d\u044b (`price_reward` \u0441\u0442\u0438\u043c\u0443\u043b\u0438\u0440\u0443\u0435\u0442 \u0443\u0434\u0435\u0440\u0436\u0430\u043d\u0438\u0435 \u043f\u0440\u0438 \u0440\u043e\u0441\u0442\u0435 \u0446\u0435\u043d\u044b) \u0438 \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u044f \u0441\u0434\u0435\u043b\u043e\u043a (`trade_reward` \u0441\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u0435\u0442 \u0438\u0442\u043e\u0433\u043e\u0432\u0443\u044e \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u0441\u0434\u0435\u043b\u043a\u0438 \u0441 \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0439).\n\n3. **\u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0437\u0430\u0446\u0438\u044f \u043f\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u044f:**\n   - \u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b `window_size` (\u0440\u0430\u0437\u043c\u0435\u0440 \u043e\u043a\u043d\u0430) \u0438 `max_shares` \u043e\u0431\u043b\u0435\u0433\u0447\u0430\u044e\u0442 \u0430\u0434\u0430\u043f\u0442\u0430\u0446\u0438\u044e \u0441\u0440\u0435\u0434\u044b \u043f\u043e\u0434 \u0440\u0430\u0437\u043d\u044b\u0435 \u0437\u0430\u0434\u0430\u0447\u0438.\n\n4. **\u041f\u043e\u043b\u043d\u044b\u0439 `info`:**\n   - \u0414\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u043e `info`, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0449\u0435\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u043e \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f\u0445, \u0442\u0435\u043a\u0443\u0449\u0435\u0439 \u0438 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u0439 \u0446\u0435\u043d\u0435, \u0432\u0441\u0435\u0445 \u0442\u0438\u043f\u0430\u0445 \u043d\u0430\u0433\u0440\u0430\u0434, \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u0438 \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044f.\n\n---\n\n### **\u041f\u0440\u0438\u043c\u0435\u0440 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0441\u0440\u0435\u0434\u044b**\n\n\u0422\u0435\u043f\u0435\u0440\u044c \u043c\u043e\u0436\u043d\u043e \u043f\u0440\u043e\u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043e\u0431\u043d\u043e\u0432\u043b\u0451\u043d\u043d\u0443\u044e \u0441\u0440\u0435\u0434\u0443:\n\n```python\nimport pandas as pd\nimport numpy as np\n\n# \u0413\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0444\u0438\u043a\u0442\u0438\u0432\u043d\u044b\u0445 \u0440\u044b\u043d\u043e\u0447\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445\ndata = pd.DataFrame({\n    \"Open\": np.random.rand(100) * 100,\n    \"High\": np.random.rand(100) * 100,\n    \"Low\": np.random.rand(100) * 100,\n    \"Close\": np.random.rand(100) * 100,\n})\n\n# \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0441\u0440\u0435\u0434\u044b \u0441 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0438\u0435\u043c \u043d\u0430 \u043e\u0434\u043d\u0443 \u0430\u043a\u0446\u0438\u044e\nenv = StockTradingEnv(data, initial_balance=1000, window_size=10, max_shares=1)\n\n# \u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0440\u0435\u0434\u044b\nstate = env.reset()\ntotal_reward = 0\n\nfor _ in range(50):\n    action = env.action_space.sample()  # \u0421\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435\n    state, reward, done, info = env.step(action)  # \u0428\u0430\u0433 \u0432 \u0441\u0440\u0435\u0434\u0435\n    \n    # \u0412\u044b\u0432\u043e\u0434 \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0439 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438\n    print(f\"Step {info['step']} | Action: {info['action']} | Balance: {info['balance']:.2f} | Shares: {info['shares']} | Net Worth: {info['net_worth']:.2f} | Reward: {info['total_reward']:.4f}\")\n    \n    total_reward += reward\n    if done:\n        break\n\nprint(f\"Total reward after test: {total_reward}\")\n```\n\n---\n\n### **\u0418\u0442\u043e\u0433\u0438**\n\n1. **\u0421\u0440\u0435\u0434\u0430 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0435\u0442 \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044e \u043d\u0430\u0433\u0440\u0430\u0434, \u0447\u0442\u043e \u043f\u043e\u043c\u043e\u0433\u0430\u0435\u0442 \u0430\u0433\u0435\u043d\u0442\u0443 \u0438\u0437\u0431\u0435\u0436\u0430\u0442\u044c \u043f\u0440\u043e\u0431\u043b\u0435\u043c \u0441 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0430\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439.**\n2. **\u041b\u0438\u043c\u0438\u0442 \u043d\u0430 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0430\u043a\u0446\u0438\u0439 (`max_shares`) \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0441\u043e\u0437\u0434\u0430\u0432\u0430\u0442\u044c \u0431\u043e\u043b\u0435\u0435 \u0440\u0435\u0430\u043b\u0438\u0441\u0442\u0438\u0447\u043d\u044b\u0435 \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u0438 \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u0435\u043c.**\n3. **`info` \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0432\u0441\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u043f\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u044f \u0430\u0433\u0435\u043d\u0442\u0430 \u0432 \u0441\u0438\u043c\u0443\u043b\u044f\u0446\u0438\u0438.**\n\n\u0415\u0441\u043b\u0438 \u0445\u043e\u0442\u0438\u0442\u0435 \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u0438\u043b\u0438 \u0443\u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u0441\u0442\u0432\u043e\u0432\u0430\u0442\u044c, \u0434\u0430\u0439\u0442\u0435 \u0437\u043d\u0430\u0442\u044c! \ud83d\ude0a \ud83d\ude80","behavior":"degenerate_repetition","conversation_hash":"e048b8522aacb35e84e9c8cbe6884f87","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-11-20","n_chars":7540,"n_turns_in_conversation":34,"n_words":799,"source_row_offset":19856,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:25:35 GMT","turn_id":"dd06f89dc80a8c273fc347e1","turn_index":29,"user_text":"\u0434\u0430\u0432\u0430\u0439 \u0435\u0449\u0451 \u0440\u0430\u0437 \u043f\u043e\u043b\u043d\u044b\u0439 \u043a\u043e\u0434 \u0441\u0440\u0435\u0434\u044b, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0443 \u043d\u0430\u0441 \u043f\u043e\u043b\u0443\u0447\u0438\u043b\u0430\u0441\u044c"},{"assistant_initiates":false,"asst_text":"\u041a\u043e\u043d\u0435\u0447\u043d\u043e! \u0422\u0430\u043a\u043e\u0439 \u043f\u043e\u0434\u0445\u043e\u0434 \u043a \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044e \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0441\u0435\u0442\u0438 \u0430\u0433\u0435\u043d\u0442\u0430 \u043d\u0430\u043c\u043d\u043e\u0433\u043e \u0447\u0438\u0449\u0435 \u0438 \u043f\u0440\u043e\u0449\u0435. \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043f\u0440\u0438\u0432\u0435\u0434\u0451\u043d\u043d\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u043b\u044f \u0443\u043b\u0443\u0447\u0448\u0435\u043d\u0438\u044f \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u0432\u0430\u0448\u0435\u0433\u043e \u0430\u0433\u0435\u043d\u0442\u0430. \u0422\u0430\u043a \u043a\u0430\u043a \u043c\u044b \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u043c \u0441 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u043c\u0438 \u0440\u044f\u0434\u0430\u043c\u0438 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0440\u044b\u043d\u043e\u0447\u043d\u044b\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u0441 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c\u0438 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c\u0438), \u0430 \u043d\u0435 \u043f\u0440\u043e\u0441\u0442\u043e \u0441 `num_inputs`, \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043e\u043b\u0436\u043d\u0430 \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u0442\u044c **\u0440\u0430\u0437\u043c\u0435\u0440 \u043e\u043a\u043d\u0430** (`window_size`) \u0438 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 (`num_features`), \u043f\u043e\u0434\u0445\u043e\u0434\u044f\u0449\u0438\u0445 \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0441\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0435\u043c.\n\n\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c \u0432\u0430\u0448\u0435 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u0432 \u0438\u0441\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u043d\u043e\u043c \u0432\u0438\u0434\u0435 \u0434\u043b\u044f \u0430\u0433\u0435\u043d\u0442\u0430 Actor-Critic, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0431\u0443\u0434\u0435\u0442 \u043e\u0431\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0442\u044c \u0432\u0445\u043e\u0434\u043d\u044b\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435.\n\n---\n\n### **\u041e\u0431\u043d\u043e\u0432\u043b\u0451\u043d\u043d\u0430\u044f \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430 \u0441\u0435\u0442\u0438 \u0430\u0433\u0435\u043d\u0442\u0430**\n\n\u041e\u0431\u043d\u043e\u0432\u0438\u043c \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0443\u044e \u0441\u0435\u0442\u044c \u0441 \u0443\u0447\u0451\u0442\u043e\u043c \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b, \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u043d\u043e\u0439 \u0432\u0430\u043c\u0438, \u0438 \u0430\u0434\u0430\u043f\u0442\u0438\u0440\u0443\u0435\u043c \u0435\u0451 \u0434\u043b\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0440\u044f\u0434\u043e\u0432 \u0438 Actor-Critic.\n\n---\n\n#### **\u041f\u043e\u043b\u043d\u044b\u0439 \u043a\u043e\u0434 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0441\u0435\u0442\u0438 \u0430\u0433\u0435\u043d\u0442\u0430**\n\n```python\nimport tensorflow as tf\nfrom tensorflow.keras import layers, Model\n\ndef create_actor_critic_model(window_size, num_features, num_actions, num_hidden=128):\n    \"\"\"\n    \u0421\u043e\u0437\u0434\u0430\u0435\u0442 \u043c\u043e\u0434\u0435\u043b\u044c \u0430\u0433\u0435\u043d\u0442\u0430 Actor-Critic.\n\n    Args:\n        window_size (int): \u0420\u0430\u0437\u043c\u0435\u0440 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u043e\u043a\u043d\u0430 (\u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0448\u0430\u0433\u043e\u0432 \u043d\u0430\u0437\u0430\u0434).\n        num_features (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 (\u0444\u0438\u0447\u0435\u0439) \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u0430.\n        num_actions (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439 \u0430\u0433\u0435\u043d\u0442\u0430.\n        num_hidden (int): \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043d\u0435\u0439\u0440\u043e\u043d\u043e\u0432 \u0432 \u0441\u043a\u0440\u044b\u0442\u044b\u0445 \u0441\u043b\u043e\u044f\u0445.\n\n    Returns:\n        model: Keras Model \u0434\u043b\u044f Actor-Critic.\n    \"\"\"\n    # \u0412\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435: \u043e\u043a\u043d\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u0430\n    inputs = layers.Input(shape=(window_size, num_features))  # (batch, window_size, num_features)\n\n    # \u041e\u0431\u0449\u0430\u044f \u0447\u0430\u0441\u0442\u044c (\u0441\u0432\u0435\u0440\u0442\u043a\u0430 \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u0430)\n    common = layers.Conv1D(filters=64, kernel_size=3, activation=\"relu\")(inputs)  # \u0421\u0432\u0435\u0440\u0442\u043a\u0430\n    common = layers.Flatten()(common)  # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u0435\u043c \u0432 \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u0439 \u0442\u0435\u043d\u0437\u043e\u0440\n    common = layers.Dense(num_hidden, activation=\"relu\")(common)  # \u0421\u043a\u0440\u044b\u0442\u044b\u0439 \u0441\u043b\u043e\u0439\n\n    # \u0412\u044b\u0445\u043e\u0434 Actor-\u0430 (\u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439)\n    action = layers.Dense(num_actions, activation=\"softmax\", name=\"action_output\")(common)\n\n    # \u0412\u044b\u0445\u043e\u0434 Critic-\u0430 (\u043e\u0446\u0435\u043d\u043a\u0430 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f)\n    critic = layers.Dense(1, name=\"critic_output\")(common)\n\n    # \u0421\u0431\u043e\u0440\u043a\u0430 \u043c\u043e\u0434\u0435\u043b\u0438\n    model = Model(inputs=inputs, outputs=[action, critic])\n    return model\n```\n\n---\n\n#### **\u041e\u0431\u044a\u044f\u0441\u043d\u0435\u043d\u0438\u0435 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u0441\u0435\u0442\u0438**\n\n1. **\u0412\u0445\u043e\u0434 (`inputs`):**\n   - \u0420\u0430\u0437\u043c\u0435\u0440: `(batch_size, window_size, num_features)`.\n   - \u041f\u0440\u0438\u043c\u0435\u0440: `(1, 10, 6)` \u2014 \u044d\u0442\u043e \u043e\u043a\u043d\u043e \u0440\u0430\u0437\u043c\u0435\u0440\u043e\u043c 10 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0448\u0430\u0433\u043e\u0432 \u0441 6 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c\u0438 \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0448\u0430\u0433\u0430.\n   - \u041f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u0447\u0442\u043e \u0430\u043a\u0442\u0451\u0440\u0443-\u043a\u0440\u0438\u0442\u0438\u043a\u0443 \u043d\u0443\u0436\u043d\u043e \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u0437\u0430 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 `window_size` \u0448\u0430\u0433\u043e\u0432.\n\n2. **\u041e\u0431\u0449\u0430\u044f \u0447\u0430\u0441\u0442\u044c (`common`):**\n   - **`Conv1D` (\u0441\u0432\u0435\u0440\u0442\u043a\u0430):** \n     - \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0434\u043b\u044f \u0438\u0437\u0432\u043b\u0435\u0447\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u043e-\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0432\u0437\u0430\u0438\u043c\u043e\u0441\u0432\u044f\u0437\u0435\u0439 \u0438\u0437 \u043e\u043a\u043d\u0430 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u0430.\n     - \u0424\u0438\u043b\u044c\u0442\u0440\u044b = 64, kernel_size = 3.\n   - **`Flatten:`** \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u0435\u0442 \u0432\u044b\u0445\u043e\u0434 \u043f\u043e\u0441\u043b\u0435 \u0441\u0432\u0435\u0440\u0442\u043e\u0447\u043d\u043e\u0433\u043e \u0441\u043b\u043e\u044f \u0432 \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432.\n   - **`Dense` (\u043f\u043e\u043b\u043d\u043e\u0441\u0432\u044f\u0437\u043d\u044b\u0439 \u0441\u043b\u043e\u0439):**\n     - \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0432\u044b\u0445\u043e\u0434\u043e\u0432 = `num_hidden` (\u043e\u0431\u044b\u0447\u043d\u043e 128 \u0438\u043b\u0438 256).\n     - \u0410\u043a\u0442\u0438\u0432\u0430\u0446\u0438\u044f ReLU.\n\n3. **Actor (\u0432\u044b\u0445\u043e\u0434):**\n   - **`Dense` (\u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0432\u044b\u0445\u043e\u0434\u043e\u0432 = num_actions):**\n     - \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 softmax, \u0447\u0442\u043e\u0431\u044b \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0435\u0439 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439.\n     - \u042d\u0442\u043e \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e \u0434\u043b\u044f RL-\u0430\u0433\u0435\u043d\u0442\u043e\u0432.\n\n4. **Critic (\u0432\u044b\u0445\u043e\u0434):**\n   - **`Dense` (\u043e\u0434\u043d\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435):**\n     - \u041e\u0446\u0435\u043d\u0438\u0432\u0430\u0435\u0442 \u0442\u0435\u043a\u0443\u0449\u0435\u0435 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u0430\u0433\u0435\u043d\u0442\u0430 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0447\u0438\u0441\u0442\u0443\u044e \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044f).\n\n---\n\n#### **\u0418\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0438**\n\n\u041c\u043e\u0434\u0435\u043b\u044c \u0438 \u0435\u0451 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b \u0443\u0442\u043e\u0447\u043d\u044f\u044e\u0442\u0441\u044f \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u0441\u0440\u0435\u0434\u044b. \u041d\u0438\u0436\u0435 \u043f\u0440\u0438\u043c\u0435\u0440 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0438 \u0430\u0433\u0435\u043d\u0442\u0430:\n\n```python\n# \u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b \u0441\u0440\u0435\u0434\u044b\nwindow_size = env.observation_space.shape[0]  # \u041e\u043a\u043d\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u0430 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, 10)\nnum_features = env.observation_space.shape[1]  # \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, 6)\nnum_actions = env.action_space.n  # \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, 3: Hold, Buy, Sell)\n\n# \u0421\u043e\u0437\u0434\u0430\u0451\u043c \u043c\u043e\u0434\u0435\u043b\u044c \u0430\u0433\u0435\u043d\u0442\u0430\nmodel = create_actor_critic_model(window_size, num_features, num_actions)\n\n# \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u043c\u043e\u0434\u0435\u043b\u0438\nmodel.summary()\n```\n\n---\n\n### **\u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438**\n\n\u0414\u043b\u044f \u0442\u0435\u0441\u0442\u0430 \u043f\u0435\u0440\u0435\u0434\u0430\u0434\u0438\u043c \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0435 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u0435, \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0435\u0435 \u0441\u043f\u0435\u0446\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u043e\u043a\u043d\u0430 (`window_size`, `num_features`):\n```python\n# \u0413\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0433\u043e \u0432\u0445\u043e\u0434\u0430\ndummy_state = np.random.rand(1, window_size, num_features).astype(np.float32)  # (batch_size=1, window_size, features)\n\n# \u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u043e\u0442 \u043c\u043e\u0434\u0435\u043b\u0438\naction_probs, critic_value = model(dummy_state)\n\nprint(\"Action Probabilities:\", action_probs.numpy())  # \u0412\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0438 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439\nprint(\"Critic Value:\", critic_value.numpy())  # \u041e\u0446\u0435\u043d\u043a\u0430 \u0442\u0435\u043a\u0443\u0449\u0435\u0433\u043e \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f\n```\n\n---\n\n### **\u041f\u0440\u0438\u043c\u0435\u0440 \u0432\u044b\u0445\u043e\u0434\u0430**\n\n\u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u0440\u0430\u0437\u0431\u0435\u0440\u0451\u043c, \u0447\u0442\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u0432\u0435\u0440\u043d\u0451\u0442:\n- **Action Probabilities:**\n  ```python\n  [[0.80, 0.15, 0.05]]\n  ```\n  \u042d\u0442\u043e \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0435\u0439. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0430\u0433\u0435\u043d\u0442 \u0441\u043a\u043e\u0440\u0435\u0435 \u0432\u0441\u0435\u0433\u043e \u043f\u0440\u0438\u043c\u0435\u0442 \u0440\u0435\u0448\u0435\u043d\u0438\u0435 `Hold (0)`.\n\n- **Critic Value:**\n  ```python\n  [[25.34]]\n  ```\n  \u042d\u0442\u043e \u043e\u0446\u0435\u043d\u043a\u0430 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u0442\u0435\u043a\u0443\u0449\u0435\u0439 \u0441\u0440\u0435\u0434\u044b (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0447\u0438\u0441\u0442\u0430\u044f \u0441\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044f).\n\n---\n\n### **\u041e\u0441\u043e\u0431\u0435\u043d\u043d\u043e\u0441\u0442\u0438**\n1. **\u041f\u043e\u043b\u043d\u043e\u0435 \u0440\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435 Actor \u0438 Critic:**\n   - \u041e\u0431\u0449\u0438\u0439 \u0441\u043a\u0440\u044b\u0442\u044b\u0439 \u0441\u043b\u043e\u0439 \u043f\u0435\u0440\u0435\u0434 \u0440\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435\u043c \u043d\u0430 \u0430\u043a\u0442\u0451\u0440\u0430 \u0438 \u043a\u0440\u0438\u0442\u0438\u043a\u0430:\n     - \u041e\u043f\u0442\u0438\u043c\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0440\u044f\u0434\u043e\u0432.\n\n2. **\u0413\u0438\u0431\u043a\u043e\u0441\u0442\u044c:**\n   - \u041c\u043e\u0434\u0435\u043b\u044c \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u0430\u0434\u0430\u043f\u0442\u0438\u0440\u0443\u0435\u0442\u0441\u044f \u043f\u043e\u0434 \u0440\u0430\u0437\u043c\u0435\u0440 \u043e\u043a\u043d\u0430 (`window_size`) \u0438 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 \u0441\u0440\u0435\u0434\u044b (`num_features`).\n\n3. **\u041f\u043e\u043b\u043d\u043e\u0441\u0442\u044c\u044e \u0443\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0441\u0440\u0435\u0434\u043e\u0439:**\n   - \u041d\u0435\u0442 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u0438 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b. \u041e\u043d\u0438 \u043f\u043e\u0434\u0431\u0438\u0440\u0430\u044e\u0442\u0441\u044f \u0438\u0437 \u0441\u0440\u0435\u0434\u044b.\n\n---\n\n### **\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0435: \u0438\u043d\u0442\u0435\u0433\u0440\u0430\u0446\u0438\u044f \u0441 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435\u043c**\n\n\u0422\u0435\u043f\u0435\u0440\u044c \u044d\u0442\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u043c\u043e\u0436\u043d\u043e \u0438\u043d\u0442\u0435\u0433\u0440\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0441 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u043c \u0446\u0438\u043a\u043b\u043e\u043c, \u0447\u0442\u043e\u0431\u044b \u0430\u0433\u0435\u043d\u0442 \u043f\u0440\u0438\u043d\u0438\u043c\u0430\u043b \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0439 Actor-\u0430 (`action_probs`) \u0438 \u0443\u043b\u0443\u0447\u0448\u0430\u043b \u0441\u0432\u043e\u0438 \u043e\u0446\u0435\u043d\u043a\u0438 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0439 Critic-\u0430 (`critic_value`).\n\n\u041f\u0435\u0440\u0435\u0445\u043e\u0434\u0438\u043c \u043a \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c\u0443 \u0448\u0430\u0433\u0443 \u0438\u043b\u0438 \u0443\u0442\u043e\u0447\u043d\u044f\u0435\u043c, \u0435\u0441\u043b\u0438 \u0447\u0442\u043e-\u0442\u043e \u043e\u043a\u0430\u0437\u0430\u043b\u043e\u0441\u044c \u043d\u0435\u043f\u043e\u043d\u044f\u0442\u043d\u044b\u043c \ud83d\ude0a\ud83d\ude80!","behavior":"degenerate_repetition","conversation_hash":"e048b8522aacb35e84e9c8cbe6884f87","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-11-20","n_chars":5781,"n_turns_in_conversation":34,"n_words":652,"source_row_offset":19857,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:25:35 GMT","turn_id":"152390fd1383782fa1950d0d","turn_index":30,"user_text":"\u0434\u0430\u0432\u0430\u0439 \u043d\u0435\u0439\u0440\u043e\u043d\u043a\u0443 \u043d\u0430\u0448\u0435\u0433\u043e \u0430\u0433\u0435\u043d\u0442\u0430 \u043f\u0435\u0440\u0435\u043f\u0438\u0448\u0435\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u0432 \u0442\u0430\u043a\u043e\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u0435:\nnum_inputs = 4\nnum_actions = 2\nnum_hidden = 128\n\ninputs = layers.Input(shape=(num_inputs,))\ncommon = layers.Dense(num_hidden, activation=\"relu\")(inputs)\naction = layers.Dense(num_actions, activation=\"softmax\")(common)\ncritic = layers.Dense(1)(common)\n\nmodel = keras.Model(inputs=inputs, outputs=[action, critic])"},{"assistant_initiates":false,"asst_text":"\u041a\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0445\u043e\u0440\u043e\u0448\u043e \u0438 \u043e\u0440\u0433\u0430\u043d\u0438\u0437\u043e\u0432\u0430\u043d\u043d\u043e! \u042d\u0442\u043e \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0430\u0433\u0435\u043d\u0442\u0430 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e Actor-Critic, \u0433\u0434\u0435 **Actor** (\u0430\u043a\u0442\u0435\u0440) \u0443\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044f\u043c\u0438 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439, \u0430 **Critic** (\u043a\u0440\u0438\u0442\u0438\u043a) \u043e\u0446\u0435\u043d\u0438\u0432\u0430\u0435\u0442 \u0446\u0435\u043d\u043d\u043e\u0441\u0442\u044c \u0442\u0435\u043a\u0443\u0449\u0435\u0433\u043e \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f. \u041e\u0434\u043d\u0430\u043a\u043e \u0435\u0441\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043c\u043e\u043c\u0435\u043d\u0442\u043e\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0436\u043d\u043e \u0443\u0442\u043e\u0447\u043d\u0438\u0442\u044c, \u0443\u043b\u0443\u0447\u0448\u0438\u0442\u044c \u0438\u043b\u0438 \u0437\u0430\u043c\u0435\u0442\u0438\u0442\u044c. \u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u0440\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u043f\u043b\u044e\u0441\u044b, \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0435 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u0438 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u0438\u044f \u043f\u043e \u0443\u043b\u0443\u0447\u0448\u0435\u043d\u0438\u044e.\n\n---\n\n### **1. \u041f\u043b\u044e\u0441\u044b \u043a\u043e\u0434\u0430**\n\n1. **\u0410\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0441\u0435\u0442\u0438:**\n   - \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0441\u043b\u043e\u0439 `Conv1D` \u0434\u043b\u044f \u0438\u0437\u0432\u043b\u0435\u0447\u0435\u043d\u0438\u044f \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0435\u0439 \u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445 \u2014 \u044d\u0442\u043e \u0445\u043e\u0440\u043e\u0448\u0435\u0435 \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u043f\u043e\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a \u0440\u044b\u043d\u043e\u0447\u043d\u044b\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0440\u044f\u0434\u044b.\n   - \u041f\u043e\u043b\u043d\u043e\u0441\u0432\u044f\u0437\u043d\u044b\u0439 \u0441\u043b\u043e\u0439 (`Dense`) \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0434\u043b\u044f \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u043d\u043e\u0439 \u0440\u0430\u0431\u043e\u0442\u044b Actor-\u0430 \u0438 Critic-\u0430.\n\n2. **\u0410\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f Actor-Critic:**\n   - \u041a\u043e\u0434 \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e \u0440\u0435\u0430\u043b\u0438\u0437\u0443\u0435\u0442 \u0441\u043e\u0432\u043c\u0435\u0449\u0435\u043d\u0438\u0435 \u0440\u0430\u0441\u0447\u0435\u0442\u0430 \u043f\u043e\u0442\u0435\u0440\u044c \u0434\u043b\u044f Actor-\u0430 (\u043b\u043e\u0433\u0430\u0440\u0438\u0444\u043c\u044b \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0435\u0439 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439) \u0438 Critic-\u0430 (Huber Loss).\n\n3. **\u041f\u043e\u043d\u044f\u0442\u043d\u044b\u0439 \u0446\u0438\u043a\u043b \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f:**\n   - \u0426\u0438\u043a\u043b \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043e\u0440\u0433\u0430\u043d\u0438\u0437\u043e\u0432\u0430\u043d \u0447\u0451\u0442\u043a\u043e, \u0441 \u0448\u0430\u0433\u0430\u043c\u0438:\n     - \u0417\u0430\u043f\u0443\u0441\u043a \u0441\u0440\u0435\u0434\u044b.\n     - \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439.\n     - \u041e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0447\u0435\u0440\u0435\u0437 \u0434\u0438\u0441\u043a\u043e\u043d\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u0443\u044e \u043d\u0430\u0433\u0440\u0430\u0434\u0443 (`returns`).\n   - \u041d\u0430\u0433\u0440\u0430\u0434\u044b \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0443\u044e\u0442\u0441\u044f, \u0447\u0442\u043e\u0431\u044b \u0438\u0437\u0431\u0435\u0436\u0430\u0442\u044c \u043f\u0440\u043e\u0431\u043b\u0435\u043c \u0441 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0430\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439.\n\n4. **\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f `huber_loss`:**\n   - Huber Loss \u0445\u043e\u0440\u043e\u0448 \u0434\u043b\u044f \u043a\u0440\u0438\u0442\u0438\u043a\u0430, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u043e\u043d \u0441\u043d\u0438\u0436\u0430\u0435\u0442 \u0432\u043b\u0438\u044f\u043d\u0438\u0435 \u0432\u044b\u0431\u0440\u043e\u0441\u043e\u0432 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043d\u0435\u043e\u0436\u0438\u0434\u0430\u043d\u043d\u044b\u0445 \u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u043d\u0430\u0433\u0440\u0430\u0434).\n\n5. **\u041f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u043d\u043e\u0433\u043e \u0432\u044b\u0431\u043e\u0440\u0430 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f:**\n   - \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 softmax \u0438 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u043d\u043e\u0433\u043e \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0434\u043b\u044f \u0431\u043e\u043b\u044c\u0448\u0438\u043d\u0441\u0442\u0432\u0430 \u0437\u0430\u0434\u0430\u0447 RL.\n\n---\n\n### **2. \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0435 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u0438 \u0443\u043b\u0443\u0447\u0448\u0435\u043d\u0438\u044f**\n\n#### **2.1. \u041d\u0435\u043e\u043f\u0442\u0438\u043c\u0430\u043b\u044c\u043d\u0430\u044f \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f (learning rate):**\n   - \u0422\u0435\u043a\u0443\u0449\u0438\u0439 `learning_rate = 0.01` \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u0431\u043e\u043b\u044c\u0448\u0438\u043c \u0434\u043b\u044f RL-\u0430\u0433\u0435\u043d\u0442\u0430. \u041e\u0431\u044b\u0447\u043d\u043e \u0434\u043b\u044f Actor-Critic \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442\u0441\u044f \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0435 `0.0001 \u2013 0.001`.\n   - \u0411\u043e\u043b\u044c\u0448\u0430\u044f \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0436\u0435\u0442 \u043f\u0440\u0438\u0432\u043e\u0434\u0438\u0442\u044c \u043a \u043d\u0435\u0441\u0442\u0430\u0431\u0438\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0438 \"\u0441\u043a\u0430\u0447\u043a\u0430\u043c\" \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f.\n\n   **\u0420\u0435\u0448\u0435\u043d\u0438\u0435:**\n   ```python\n   optimizer = keras.optimizers.Adam(learning_rate=0.001)\n   ```\n\n---\n\n#### **2.2. \u041d\u0435 \u043e\u0447\u0435\u043d\u044c \u0433\u043b\u0443\u0431\u043e\u043a\u0430\u044f \u0441\u0435\u0442\u044c**\n   - \u0415\u0441\u043b\u0438 \u0440\u044b\u043d\u043e\u0447\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0438\u043c\u0435\u044e\u0442 \u0441\u043b\u043e\u0436\u043d\u044b\u0435 \u043f\u0430\u0442\u0442\u0435\u0440\u043d\u044b (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043c\u043d\u043e\u0433\u043e \u0444\u0438\u0447\u0435\u0439 \u0438\u043b\u0438 \u0434\u043b\u0438\u043d\u043d\u044b\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0440\u044f\u0434\u044b), \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e, Current Conv1D \u0441 \u043e\u0434\u043d\u0438\u043c \u0441\u043b\u043e\u0435\u043c \u043d\u0435\u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u0435\u043d \u0434\u043b\u044f \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u044f \u0441\u043b\u043e\u0436\u043d\u044b\u0445 \u0441\u0432\u044f\u0437\u0435\u0439.\n\n   **\u0420\u0435\u0448\u0435\u043d\u0438\u0435:**\n   \u0414\u043e\u0431\u0430\u0432\u044c\u0442\u0435 \u0435\u0449\u0451 \u043e\u0434\u0438\u043d \u0441\u0432\u0435\u0440\u0442\u043e\u0447\u043d\u044b\u0439 \u0438\u043b\u0438 LSTM \u0441\u043b\u043e\u0439 \u0434\u043b\u044f \u0443\u043b\u0443\u0447\u0448\u0435\u043d\u0438\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u043f\u0430\u0442\u0442\u0435\u0440\u043d\u043e\u0432:\n   ```python\n   common = layers.Conv1D(filters=128, kernel_size=3, activation=\"relu\")(common)\n   ```\n\n---\n\n#### **2.3. \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u0430\u044f \u043f\u043e\u0442\u0435\u0440\u044f \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u043e\u0432**\n   - \u0415\u0441\u043b\u0438 `action_probs` \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u043e\u0447\u0435\u043d\u044c \u043c\u0430\u043b\u0435\u043d\u044c\u043a\u0438\u0435 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0438 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \\( p(a|s) \\approx 10^{-8} \\)), \u0442\u043e \\(\\log(p(a|s))\\) \u043c\u043e\u0436\u0435\u0442 \u0441\u0442\u0430\u0442\u044c \u043e\u0447\u0435\u043d\u044c \u0431\u043e\u043b\u044c\u0448\u0438\u043c \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u043c \u0447\u0438\u0441\u043b\u043e\u043c, \u0447\u0442\u043e \u043f\u0440\u0438\u0432\u0435\u0434\u0451\u0442 \u043a \u043d\u0435\u0441\u0442\u0430\u0431\u0438\u043b\u044c\u043d\u043e\u0441\u0442\u0438.\n\n   **\u0420\u0435\u0448\u0435\u043d\u0438\u0435:**\n   \u041e\u0433\u0440\u0430\u043d\u0438\u0447\u044c\u0442\u0435 \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0435\u0439:\n   ```python\n   action_probs = tf.clip_by_value(action_probs, 1e-10, 1.0)\n   ```\n\n---\n\n#### **2.4. \u041e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0438\u0435 \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438 `grads`:**\n   - TensorFlow \u0438\u043d\u043e\u0433\u0434\u0430 \u043c\u043e\u0436\u0435\u0442 \u0432\u0435\u0440\u043d\u0443\u0442\u044c `None` \u0438\u043b\u0438 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u044b. \u042d\u0442\u043e \u043d\u0443\u0436\u043d\u043e \u043f\u0440\u043e\u0432\u0435\u0440\u044f\u0442\u044c \u043f\u0435\u0440\u0435\u0434 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435\u043c \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u043e\u0432:\n   ```python\n   grads = tape.gradient(loss_value, model.trainable_variables)\n   if grads is None or None in grads:\n       print(\"Warning: Gradients are missing\")\n   optimizer.apply_gradients(zip(grads, model.trainable_variables))\n   ```\n\n---\n\n#### **2.5. \u0420\u0438\u0441\u043a \"Exploding Returns\":**\n   - \u0415\u0441\u043b\u0438 \u043d\u0430\u0433\u0440\u0430\u0434\u044b \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u0432\u0435\u043b\u0438\u043a\u0438 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0431\u043e\u043b\u044c\u0448\u0435 100 \u0438\u043b\u0438 \u043c\u0435\u043d\u044c\u0448\u0435 \\(-100\\)), \u044d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0432\u044b\u0437\u0432\u0430\u0442\u044c \u043d\u0435\u0441\u0442\u0430\u0431\u0438\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u0438 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438 Critic-\u0430.\n\n   **\u0420\u0435\u0448\u0435\u043d\u0438\u0435:**\n   \u0414\u043b\u044f \u0441\u0442\u0430\u0431\u0438\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u044c\u0442\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043d\u0430\u0433\u0440\u0430\u0434 \u043f\u0435\u0440\u0435\u0434 \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0435\u0439. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440:\n   ```python\n   rewards_history = np.clip(rewards_history, -10, 10)  # \u041e\u0433\u0440\u0430\u043d\u0438\u0447\u0438\u0432\u0430\u0435\u043c \u043d\u0430\u0433\u0440\u0430\u0434\u044b\n   ```\n\n---\n\n#### **2.6. \u0412\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0435 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u0437\u0430\u0441\u0442\u043e\u044f \u0430\u0433\u0435\u043d\u0442\u0430:**\n   - \u0415\u0441\u043b\u0438 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f `Hold (0)` \u043f\u043e\u0441\u0442\u0435\u043f\u0435\u043d\u043d\u043e \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u0441\u044f \u0431\u043b\u0438\u0437\u043a\u043e\u0439 \u043a 1, \u0430\u0433\u0435\u043d\u0442 \u043c\u043e\u0436\u0435\u0442 \"\u0437\u0430\u0441\u0442\u0440\u044f\u0442\u044c\" \u0432 \u043e\u0434\u043d\u043e\u043c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0438.\n\n   **\u0420\u0435\u0448\u0435\u043d\u0438\u0435:**\n   \u0414\u043e\u0431\u0430\u0432\u044c\u0442\u0435 \u0448\u0443\u043c, \u0447\u0442\u043e\u0431\u044b \u0441\u0442\u0438\u043c\u0443\u043b\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0430\u0433\u0435\u043d\u0442\u0430 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u044c \u0434\u0440\u0443\u0433\u0438\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f (exploration):\n   ```python\n   action = np.random.choice(\n       num_actions, p=tf.squeeze(action_probs).numpy() * (1 - epsilon) + epsilon / num_actions\n   )\n   ```\n\n---\n\n### **3. \u041e\u0431\u0449\u0438\u0435 \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u0438**\n\n#### **3.1. \u041e\u0431\u043e\u0433\u0430\u0449\u0435\u043d\u0438\u0435 \u0432\u044b\u0432\u043e\u0434\u0430 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f**\n   - \u041b\u043e\u0433\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0437\u0430 \u043a\u0430\u0436\u0434\u044b\u0435 10 \u044d\u043f\u0438\u0437\u043e\u0434\u043e\u0432 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u043e \u0442\u043e\u043b\u044c\u043a\u043e `running_reward`. \u0414\u043e\u0431\u0430\u0432\u044c\u0442\u0435 \u0431\u043e\u043b\u044c\u0448\u0435 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440:\n     - \u0421\u0440\u0435\u0434\u043d\u044e\u044e \u043d\u0430\u0433\u0440\u0430\u0434\u0443 (`np.mean(rewards_history)`).\n     - \u041c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u0443\u044e/\u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0443\u044e \u043d\u0430\u0433\u0440\u0430\u0434\u0443.\n   ```python\n   if episode_count % 10 == 0:\n       print(f\"Episode {episode_count} | Running Reward: {running_reward:.2f} | Avg Reward: {np.mean(rewards_history):.2f}\")\n   ```\n\n---\n\n#### **3.2. \u0414\u043e\u0431\u0430\u0432\u044c\u0442\u0435 `epsilon decay`**\n   - \u0415\u0441\u043b\u0438 \u0430\u0433\u0435\u043d\u0442 \u0431\u0443\u0434\u0435\u0442 \u0437\u0430\u0441\u0442\u0440\u0435\u0432\u0430\u0442\u044c \u0432 \u043d\u0435\u0443\u0434\u0430\u0447\u043d\u044b\u0445 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f\u0445, \u043c\u043e\u0436\u043d\u043e \u0434\u043e\u0431\u0430\u0432\u0438\u0442\u044c \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0435 \u0447\u0435\u0440\u0435\u0437 \u044d\u043f\u0441\u0438\u043b\u043e\u043d \u0436\u0430\u0434\u043d\u043e\u0441\u0442\u044c (`epsilon greedy`).\n   ```python\n   epsilon = max(0.1, epsilon * 0.995)  # \u0423\u043c\u0435\u043d\u044c\u0448\u0430\u0435\u043c epsilon \u043f\u043e\u0441\u043b\u0435 \u043a\u0430\u0436\u0434\u043e\u0439 \u0438\u0442\u0435\u0440\u0430\u0446\u0438\u0438\n   ```\n\n---\n\n### **4. \u0418\u0442\u043e\u0433\u043e\u0432\u0430\u044f \u0432\u0435\u0440\u0441\u0438\u044f \u0446\u0438\u043a\u043b\u0430 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f**\n\n```python\noptimizer = keras.optimizers.Adam(learning_rate=0.001)  # \u041b\u0443\u0447\u0448\u0435 \u0443\u043c\u0435\u043d\u044c\u0448\u0438\u0442\u044c learning rate\nepsilon = 1.0  # \u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f (\u043d\u0430\u0447\u0430\u043b\u044c\u043d\u044b\u0439)\nrunning_reward = 0\n\nwhile True:\n    state = env.reset()\n    episode_reward = 0\n    with tf.GradientTape() as tape:\n        for timestep in range(1, max_steps_per_episode):\n            state = ops.convert_to_tensor(state, dtype=tf.float32)\n            state = ops.expand_dims(state, 0)\n\n            # \u041f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u0443\u0435\u043c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f\n            action_probs, critic_value = model(state)\n            action_probs = tf.clip_by_value(action_probs, 1e-10, 1.0)\n            \n            # \u0412\u044b\u0431\u043e\u0440 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f\n            action = np.random.choice(num_actions, p=np.squeeze(action_probs.numpy()))\n\n            action_probs_history.append(tf.math.log(action_probs[0, action]))\n            critic_value_history.append(critic_value[0, 0])\n\n            # \u0414\u0435\u0439\u0441\u0442\u0432\u0438\u0435 \u0432 \u0441\u0440\u0435\u0434\u0435\n            state, reward, done, _ = env.step(action)\n\n            rewards_history.append(reward)\n            episode_reward += reward\n\n            if done:\n                break\n\n        # \u0420\u0430\u0441\u0441\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u043c \u0434\u0438\u0441\u043a\u043e\u043d\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u043d\u0430\u0433\u0440\u0430\u0434\u044b\n        returns = []\n        discounted_sum = 0\n        for r in rewards_history[::-1]:\n            discounted_sum = r + gamma * discounted_sum\n            returns.insert(0, discounted_sum)\n\n        # \u041d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043d\u0430\u0433\u0440\u0430\u0434\n        returns = np.array(returns)\n        returns = (returns - np.mean(returns)) / (np.std(returns) + eps)\n\n        # \u041f\u043e\u0442\u0435\u0440\u0438\n        history = zip(action_probs_history, critic_value_history, returns)\n        actor_losses, critic_losses = [], []\n        for log_prob, value, ret in history:\n            diff = ret - value\n            actor_losses.append(-log_prob * diff)\n            critic_losses.append(huber_loss(tf.expand_dims(value, 0), tf.expand_dims(ret, 0)))\n\n        loss_value = tf.reduce_sum(actor_losses) + tf.reduce_sum(critic_losses)\n        grads = tape.gradient(loss_value, model.trainable_variables)\n        optimizer.apply_gradients(zip(grads, model.trainable_variables))\n\n        # \u041e\u0447\u0438\u0441\u0442\u043a\u0430\n        action_probs_history.clear()\n        critic_value_history.clear()\n        rewards_history.clear()\n\n    # \u041b\u043e\u0433\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435\n    running_reward = 0.05 * episode_reward + (1 - 0.05) * running_reward\n    episode_count += 1\n    if episode_count % 10 == 0:\n        print(f\"Episode {episode_count} | Running Reward: {running_reward:.2f} |\")\n\n    # \u0423\u0441\u043b\u043e\u0432\u0438\u0435 \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u044f\n    if running_reward > 500:  # \u0417\u0430\u0434\u0430\u0451\u043c \u0446\u0435\u043b\u0435\u0432\u0443\u044e \u043d\u0430\u0433\u0440\u0430\u0434\u0443\n        print(\"Solved!\")\n        break\n```\n\n---\n\n\u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u043f\u0440\u043e\u0431\u0443\u0435\u043c \u044d\u0442\u0438 \u0443\u043b\u0443\u0447\u0448\u0435\u043d\u0438\u044f, \u0438 \u0435\u0441\u043b\u0438 \u0431\u0443\u0434\u0443\u0442 \u0432\u043e\u043f\u0440\u043e\u0441\u044b \u043e \u043a\u0430\u043a\u0438\u0445-\u0442\u043e \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0430\u0445, \u043e\u0431\u044f\u0437\u0430\u0442\u0435\u043b\u044c\u043d\u043e \u043f\u0438\u0448\u0438\u0442\u0435! \ud83d\ude0a ","behavior":"degenerate_repetition","conversation_hash":"e048b8522aacb35e84e9c8cbe6884f87","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-11-20","n_chars":12166,"n_turns_in_conversation":34,"n_words":1303,"source_row_offset":19858,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:25:35 GMT","turn_id":"d3ec9a09da4a8bf8a70797f6","turn_index":31,"user_text":"\u0434\u043b\u044f \u0430\u0433\u0435\u043d\u0442\u0430 \u0434\u0430\u0432\u0430\u0439 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0432\u043e\u0442 \u044d\u0442\u043e\u0442 \u043a\u043e\u0434:\nwindow_size = env.observation_space.shape[0]  # \u0420\u0430\u0437\u043c\u0435\u0440 \u043e\u043a\u043d\u0430\nnum_features = env.observation_space.shape[1]  # \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432\naction_space_size = env.action_space.n  # \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439\n\n# Configuration parameters for the whole setup\nseed = 42\ngamma = 0.99  # Discount factor for past rewards\nmax_steps_per_episode = len(data) - window_size - 2\neps = np.finfo(np.float32).eps.item()  # Smallest number such that 1.0 + eps != 1.0\ninputs = layers.Input(shape=(window_size, num_features))  # (batch, window_size, num_features)\nnum_hidden=128\nnum_actions = env.action_space.n\n# \u041e\u0431\u0449\u0430\u044f \u0447\u0430\u0441\u0442\u044c (\u0441\u0432\u0435\u0440\u0442\u043a\u0430 \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u0430)\ncommon = layers.Conv1D(filters=64, kernel_size=3, activation=\"relu\")(inputs)  # \u0421\u0432\u0435\u0440\u0442\u043a\u0430\ncommon = layers.Flatten()(common)  # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u0435\u043c \u0432 \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u0439 \u0442\u0435\u043d\u0437\u043e\u0440\ncommon = layers.Dense(num_hidden, activation=\"relu\")(common)  # \u0421\u043a\u0440\u044b\u0442\u044b\u0439 \u0441\u043b\u043e\u0439\n# \u0412\u044b\u0445\u043e\u0434 Actor-\u0430 (\u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439)\naction = layers.Dense(num_actions, activation=\"softmax\", name=\"action_output\")(common)\n# \u0412\u044b\u0445\u043e\u0434 Critic-\u0430 (\u043e\u0446\u0435\u043d\u043a\u0430 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f)\ncritic = layers.Dense(1, name=\"critic_output\")(common)\n# \u0421\u0431\u043e\u0440\u043a\u0430 \u043c\u043e\u0434\u0435\u043b\u0438\nmodel = keras.Model(inputs=inputs, outputs=[action, critic])\noptimizer = keras.optimizers.Adam(learning_rate=0.01)\nhuber_loss = keras.losses.Huber()\naction_probs_history = []\ncritic_value_history = []\nrewards_history = []\nrunning_reward = 0\nepisode_count = 0\n\nwhile True:  # Run until solved\n    state = env.reset()\n    episode_reward = 0\n    with tf.GradientTape() as tape:\n        for timestep in range(1, max_steps_per_episode):\n            # env.render(); Adding this line would show the attempts\n            # of the agent in a pop up window.\n\n            state = ops.convert_to_tensor(state)\n            state = ops.expand_dims(state, 0)\n\n            # Predict action probabilities and estimated future rewards\n            # from environment state\n            action_probs, critic_value = model(state)\n            critic_value_history.append(critic_value[0, 0])\n\n            # Sample action from action probability distribution\n            action = np.random.choice(num_actions, p=np.squeeze(action_probs))\n            action_probs_history.append(ops.log(action_probs[0, action]))\n\n            # Apply the sampled action in our environment\n            state, reward, done, _ = env.step(action)\n            rewards_history.append(reward)\n            episode_reward += reward\n\n            if done:\n                break\n\n        # Update running reward to check condition for solving\n        running_reward = 0.05 * episode_reward + (1 - 0.05) * running_reward\n\n        # Calculate expected value from rewards\n        # - At each timestep what was the total reward received after that timestep\n        # - Rewards in the past are discounted by multiplying them with gamma\n        # - These are the labels for our critic\n        returns = []\n        discounted_sum = 0\n        for r in rewards_history[::-1]:\n            discounted_sum = r + gamma * discounted_sum\n            returns.insert(0, discounted_sum)\n\n        # Normalize\n        returns = np.array(returns)\n        returns = (returns - np.mean(returns)) / (np.std(returns) + eps)\n        returns = returns.tolist()\n\n        # Calculating loss values to update our network\n        history = zip(action_probs_history, critic_value_history, returns)\n        actor_losses = []\n        critic_losses = []\n        for log_prob, value, ret in history:\n            # At this point in history, the critic estimated that we would get a\n            # total reward = `value` in the future. We took an action with log probability\n            # of `log_prob` and ended up receiving a total reward = `ret`.\n            # The actor must be updated so that it predicts an action that leads to\n            # high rewards (compared to critic's estimate) with high probability.\n            diff = ret - value\n            actor_losses.append(-log_prob * diff)  # actor loss\n\n            # The critic must be updated so that it predicts a better estimate of\n            # the future rewards.\n            critic_losses.append(\n                huber_loss(ops.expand_dims(value, 0), ops.expand_dims(ret, 0))\n            )\n\n        # Backpropagation\n        loss_value = sum(actor_losses) + sum(critic_losses)\n        grads = tape.gradient(loss_value, model.trainable_variables)\n        optimizer.apply_gradients(zip(grads, model.trainable_variables))\n\n        # Clear the loss and reward history\n        action_probs_history.clear()\n        critic_value_history.clear()\n        rewards_history.clear()\n\n    # Log details\n    episode_count += 1\n    if episode_count % 10 == 0:\n        template = \"running reward: {:.2f} at episode {}\"\n        print(template.format(running_reward, episode_count))\n\n    if done:\n        break\n\n\u0447\u0442\u043e \u0441\u043a\u0430\u0436\u0435\u0448\u044c \u043f\u0440\u043e \u044d\u0442\u043e\u0442 \u043a\u043e\u0434?"},{"assistant_initiates":false,"asst_text":"\u041e\u0442\u043b\u0438\u0447\u043d\u043e, \u0432\u044b \u0433\u043b\u0443\u0431\u043e\u043a\u043e \u043f\u043e\u043d\u0438\u043c\u0430\u0435\u0442\u0435 \u0442\u0435\u043a\u0443\u0449\u0438\u0439 \u043f\u0440\u043e\u0446\u0435\u0441\u0441 \u0438 \u0433\u043e\u0442\u043e\u0432\u044b \u0433\u0438\u0431\u043a\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u0438\u0442\u044c \u043a \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0435 \u0430\u0433\u0435\u043d\u0442\u0430. \u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u0440\u0430\u0437\u0431\u0435\u0440\u0451\u043c \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u0435 \u043f\u0443\u043d\u043a\u0442\u044b \u0438 \u043e\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u043c\u0441\u044f \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u0435\u0435 \u043d\u0430 **\u044d\u043f\u0441\u0438\u043b\u043e\u043d-\u0436\u0430\u0434\u043d\u043e\u0441\u0442\u0438 (`epsilon-greedy`)**, \u0442\u0430\u043a \u043a\u0430\u043a \u044d\u0442\u043e \u0432\u0430\u0436\u043d\u044b\u0439 \u043c\u043e\u043c\u0435\u043d\u0442 (\u043e \u043d\u0451\u043c \u0432 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043f\u0443\u043d\u043a\u0442\u0435).\n\n---\n\n### **\u041e\u0442\u0432\u0435\u0442\u044b \u043d\u0430 \u0432\u0430\u0448\u0438 \u0432\u043e\u043f\u0440\u043e\u0441\u044b:**\n\n#### **2.2. Self-Attention**\n  - \u0414\u0430, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 **Self-Attention** \u0437\u043d\u0430\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0443\u043b\u0443\u0447\u0448\u0438\u0442 \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u043e\u0441\u0442\u044c \u0430\u0433\u0435\u043d\u0442\u0430 \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u0442\u044c \u043a\u0430\u043a \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u044f \u0437\u0430 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 \\( N \\) \u0434\u043d\u0435\u0439), \u0442\u0430\u043a \u0438 \u0432\u0437\u0430\u0438\u043c\u043e\u0441\u0432\u044f\u0437\u044c \u043c\u0435\u0436\u0434\u0443 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u043c\u0438 \u0444\u0438\u0447\u0430\u043c\u0438 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0431\u0430\u043b\u0430\u043d\u0441/\u0446\u0435\u043d\u0430). \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u043f\u043e\u0437\u0436\u0435 \u0430\u0434\u0430\u043f\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0443 \u043c\u043e\u0434\u0435\u043b\u0438 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c Attention, \u0447\u0442\u043e\u0431\u044b \u0434\u043e\u0441\u0442\u0438\u0447\u044c \u043b\u0443\u0447\u0448\u0438\u0445 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432.\n\n#### **2.3. \u041a\u0430\u043a \u0447\u0430\u0441\u0442\u043e softmax-\u0441\u0433\u043b\u0430\u0436\u0438\u0432\u0430\u043d\u0438\u0435 \u043d\u0435 \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u0432 \u0432\u0430\u0448\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0435?**\n  - \u0412 \u0441\u043b\u0443\u0447\u0430\u044f\u0445, \u043a\u043e\u0433\u0434\u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u043d\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0430 (\u043d\u0430 \u0440\u0430\u043d\u043d\u0438\u0445 \u0441\u0442\u0430\u0434\u0438\u044f\u0445), \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c **\u0447\u0440\u0435\u0437\u043c\u0435\u0440\u043d\u043e \u043f\u0435\u0440\u0435\u043a\u043e\u0448\u0435\u043d\u043d\u043e\u0439**:\n    - \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0435\u0441\u043b\u0438 `softmax` \u0432\u044b\u0434\u0430\u0451\u0442 \u0447\u0442\u043e-\u0442\u043e \u0432\u0440\u043e\u0434\u0435 `[0.999, 1e-8, 1e-8]`:\n      - \u042d\u0442\u043e \u043c\u0430\u043b\u043e\u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e (1 \u0438\u0437 10 \u0440\u0430\u0437 \u043d\u0430 \u0441\u043b\u0430\u0431\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445).\n      - \u041c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u043e\u0439, \u0435\u0441\u043b\u0438 \u0430\u0433\u0435\u043d\u0442 \"\u0437\u0430\u0441\u0442\u0440\u0435\u0432\u0430\u0435\u0442\" \u0432 \u043e\u0434\u043d\u043e\u043c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0438 (\u0431\u043e\u043b\u044c\u0448\u043e\u0439 \u043f\u0435\u0440\u0435\u043a\u043e\u0441).\n\n  \u041f\u043e\u0437\u0436\u0435 \u043f\u0440\u0438 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438 \u0442\u0430\u043a\u0438\u0445 \u0441\u043b\u0443\u0447\u0430\u0435\u0432 \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u0441\u044f \u043c\u0435\u043d\u044c\u0448\u0435, \u043a\u043e\u0433\u0434\u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u043d\u0430\u0443\u0447\u0438\u0442\u0441\u044f \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0442\u044c \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0438 \u0431\u043e\u043b\u0435\u0435 \u0440\u0430\u0432\u043d\u043e\u043c\u0435\u0440\u043d\u043e.\n\n  \u0415\u0441\u043b\u0438 \u0442\u0430\u043a\u043e\u0435 \u0441\u043b\u0443\u0447\u0430\u0435\u0442\u0441\u044f \u043d\u0435\u0447\u0430\u0441\u0442\u043e \u0438 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0430 \u0432\u0430\u043c \u043d\u0435 \u043c\u0435\u0448\u0430\u0435\u0442, \u043f\u0440\u0435\u0434\u043e\u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c (`tf.clip_by_value`) \u043e\u0441\u0442\u0430\u0432\u0438\u0442\u044c \u043c\u043e\u0436\u043d\u043e, \u043d\u043e \u0441\u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043e\u043d \u0440\u0435\u0434\u043a\u043e.\n\n#### **2.6. \u041e \u0441\u0442\u043e\u043f\u0440\u043e\u0446\u0435\u043d\u0442\u043d\u043e\u0439 \u0438\u043c\u043f\u043b\u0435\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u0438 `epsilon-greedy`**\n\u041f\u043e\u0433\u043e\u0432\u043e\u0440\u0438\u043c \u043e\u0431 \u044d\u0442\u043e\u043c \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u0435\u0435!\n\n---\n\n## **\u041f\u043e\u0433\u0440\u0443\u0436\u0430\u0435\u043c\u0441\u044f \u0432 `epsilon-greedy`**\n\n**\u0427\u0442\u043e \u0442\u0430\u043a\u043e\u0435 `\u03b5-greedy`:**\n- \u042d\u0442\u043e \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u044f \u0432\u044b\u0431\u043e\u0440\u0430 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f, \u0433\u0434\u0435:\n  - \u0421 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c\u044e \\( \\epsilon \\), \u0430\u0433\u0435\u043d\u0442 \u0432\u044b\u0431\u0438\u0440\u0430\u0435\u0442 **\u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435** (exploration, \"\u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0435\").\n  - \u0421 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c\u044e \\( 1 - \\epsilon \\), \u0430\u0433\u0435\u043d\u0442 \u0432\u044b\u0431\u0438\u0440\u0430\u0435\u0442 **\u043b\u0443\u0447\u0448\u0435\u0435 \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u043d\u043e\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435** \u043e\u0442 \u043c\u043e\u0434\u0435\u043b\u0438 (exploitation, \"\u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\").\n\n**\u041a\u0430\u043a \u044d\u0442\u043e \u043f\u043e\u043c\u043e\u0433\u0430\u0435\u0442 \u0430\u0433\u0435\u043d\u0442\u0443:**\n- \u041d\u0430 \u0440\u0430\u043d\u043d\u0438\u0445 \u044d\u0442\u0430\u043f\u0430\u0445 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f:\n  - \u0410\u0433\u0435\u043d\u0442 \u043c\u043e\u0436\u0435\u0442 \"\u043e\u0441\u043c\u0430\u0442\u0440\u0438\u0432\u0430\u0442\u044c\" \u0432\u0441\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f, \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u043d\u044f\u0442\u044c, \u043a\u0430\u043a\u0438\u0435 \u0438\u0437 \u043d\u0438\u0445 \u043f\u0440\u0438\u043d\u043e\u0441\u044f\u0442 \u043d\u0430\u0433\u0440\u0430\u0434\u0443.\n  - \u0411\u0435\u0437 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0445 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439 \u0430\u0433\u0435\u043d\u0442 \u043c\u043e\u0436\u0435\u0442 **\u0431\u044b\u0441\u0442\u0440\u043e \u0437\u0430\u0441\u0442\u0440\u044f\u0442\u044c** \u0432 \u043d\u0435\u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e\u0439 \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u0438.\n- \u041d\u0430 \u0431\u043e\u043b\u0435\u0435 \u043f\u043e\u0437\u0434\u043d\u0438\u0445 \u044d\u0442\u0430\u043f\u0430\u0445:\n  - \u0410\u0433\u0435\u043d\u0442 \u043f\u043e\u0441\u0442\u0435\u043f\u0435\u043d\u043d\u043e \u0443\u043c\u0435\u043d\u044c\u0448\u0430\u0435\u0442 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0435 (\\( \\epsilon \\rightarrow 0 \\)) \u0438 \u0444\u043e\u043a\u0443\u0441\u0438\u0440\u0443\u0435\u0442\u0441\u044f \u043d\u0430 \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e \u043d\u0430\u0439\u0434\u0435\u043d\u043d\u044b\u0445 \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u044f\u0445 (\"\u0432\u044b\u0433\u0440\u0443\u0437\u043a\u0430 \u0437\u043d\u0430\u043d\u0438\u0439\").\n\n---\n\n### **1. \u041a\u0430\u043a \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u0442\u044c `epsilon-greedy`:**\n\n1. **\u041e\u0441\u043d\u043e\u0432\u043d\u0430\u044f \u0438\u0434\u0435\u044f:**\n   - \u0412 \u043a\u0430\u0436\u0434\u044b\u0439 \u043c\u043e\u043c\u0435\u043d\u0442 \u043f\u0440\u0438\u043d\u0438\u043c\u0430\u0435\u043c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435 \u043b\u0438\u0431\u043e \u043f\u043e \u043c\u043e\u0434\u0435\u043b\u0438, \u043b\u0438\u0431\u043e \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c:\n     ```python\n     if np.random.rand() < epsilon:\n         action = np.random.randint(num_actions)  # \u0421\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435\n     else:\n         action = np.random.choice(num_actions, p=np.squeeze(action_probs))  # \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u043d\u043e\u0435\n     ```\n\n2. **\u0421\u043d\u0438\u0436\u0435\u043d\u0438\u0435 `epsilon`:**\n   - \u041f\u043e\u0441\u043b\u0435 \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u044d\u043f\u0438\u0437\u043e\u0434\u0430 \u0443\u043c\u0435\u043d\u044c\u0448\u0430\u0435\u043c `epsilon`:\n     ```python\n     epsilon = max(0.1, epsilon * 0.995)\n     ```\n     - \u0417\u0434\u0435\u0441\u044c:\n        - `0.995` \u2014 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442 \u0443\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u0438\u044f (\u0440\u0435\u0433\u0443\u043b\u0438\u0440\u0443\u0435\u0442\u0441\u044f \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0437\u0430\u0434\u0430\u0447\u0438).\n        - `0.1` \u2014 \u043d\u0438\u0436\u043d\u0438\u0439 \u0443\u0440\u043e\u0432\u0435\u043d\u044c, \u043d\u0438\u0436\u0435 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0435 \u043d\u0435 \u0431\u0443\u0434\u0435\u0442 \u0441\u043d\u0438\u0436\u0430\u0442\u044c\u0441\u044f (\u0434\u043b\u044f \u043f\u043e\u0441\u0442\u043e\u044f\u043d\u043d\u043e\u0433\u043e \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0433\u043e \u0448\u0443\u043c\u0430).\n\n3. **\u041f\u0440\u043e\u0441\u0442\u0430\u044f \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0430:**\n   - \u041d\u0430\u0447\u0430\u043b\u044c\u043d\u044b\u0439 `epsilon` (\\( \\epsilon_0 \\)) \u0437\u0430\u0434\u0430\u0451\u0442\u0441\u044f \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c:\n     ```python\n     epsilon = 1.0  # \u041d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0435 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0435 \u043f\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u0435\n     ```\n\n---\n\n### **2. \u041f\u043e\u043b\u043d\u044b\u0439 \u043f\u0440\u0438\u043c\u0435\u0440 \u0438\u043c\u043f\u043b\u0435\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u0438 \u0441 `epsilon-greedy`**\n\n```python\n# \u041d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 epsilon (\u043d\u0430 100% \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0439 \u0432\u044b\u0431\u043e\u0440 \u043d\u0430 \u043f\u0435\u0440\u0432\u044b\u0445 \u0438\u0442\u0435\u0440\u0430\u0446\u0438\u044f\u0445)\nepsilon = 1.0\nepsilon_decay = 0.995  # \u041a\u0430\u043a \u0431\u044b\u0441\u0442\u0440\u043e \u0441\u043d\u0438\u0436\u0430\u0442\u044c epsilon\nmin_epsilon = 0.1  # \u041c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043c\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 epsilon\n\nwhile True:\n    state = env.reset()\n    total_reward = 0\n\n    with tf.GradientTape() as tape:\n        for timestep in range(max_steps_per_episode):\n            # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u0435\u043c \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u0435\n            state = ops.convert_to_tensor(state, dtype=tf.float32)\n            state = ops.expand_dims(state, 0)\n\n            # \u041f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u0443\u0435\u043c \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0438 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439\n            action_probs, critic_value = model(state)\n\n            # \u0412\u044b\u0431\u043e\u0440 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f:\n            if np.random.rand() < epsilon:  # \u0421 `epsilon` \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c\u044e \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435\n                action = np.random.randint(num_actions)  # Random exploration\n                print(f\"[Exploration] Random action: {action}\")\n            else:  # \u0421 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c\u044e `1 - epsilon` \u0432\u044b\u0431\u0438\u0440\u0430\u0435\u043c \u043c\u043e\u0434\u0435\u043b\u044c\u043d\u043e\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435\n                action = np.random.choice(num_actions, p=np.squeeze(action_probs))\n                print(f\"[Exploitation] Chosen action: {action}\")\n\n            # \u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c log \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0435\u0439 \u0438 \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u043a\u0440\u0438\u0442\u0438\u043a\u0430\n            action_probs_history.append(tf.math.log(action_probs[0, action]))\n            critic_value_history.append(critic_value[0, 0])\n\n            # \u0412\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0435 \u0432 \u0441\u0440\u0435\u0434\u0435\n            next_state, reward, done, _ = env.step(action)\n            rewards_history.append(reward)\n            total_reward += reward\n\n            state = next_state  # \u041f\u0435\u0440\u0435\u0445\u043e\u0434 \u043a \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c\u0443 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044e\n\n            if done:\n                break\n\n        # \u041e\u0431\u043d\u043e\u0432\u043b\u044f\u0435\u043c running reward\n        running_reward = 0.05 * total_reward + (1 - 0.05) * running_reward\n\n        # \u0421\u043d\u0438\u0436\u0430\u0435\u043c epsilon (\u0443\u043c\u0435\u043d\u044c\u0448\u0430\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0441\u0442\u044c)\n        epsilon = max(min_epsilon, epsilon * epsilon_decay)  # \u041f\u043b\u0430\u0432\u043d\u043e \u0441\u043d\u0438\u0436\u0430\u0435\u043c\n        print(f\"> Current epsilon: {epsilon:.3f}\")\n```\n\n---\n\n### **3. \u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b `epsilon-greedy`: \u0447\u0442\u043e \u043f\u043e\u043f\u0440\u043e\u0431\u043e\u0432\u0430\u0442\u044c**\n\n1. **\u041d\u0430\u0447\u0430\u043b\u044c\u043d\u044b\u0439 `epsilon`:**\n   - `1.0`: \u0410\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u043e \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0435 \u043f\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u0435.\n   - `0.2`: \u041d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f, \u043d\u043e \u0432 \u043e\u0441\u043d\u043e\u0432\u043d\u043e\u043c \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435 \u043d\u0430 \u0432\u043e\u0437\u043d\u0430\u0433\u0440\u0430\u0436\u0434\u0435\u043d\u0438\u0435 \u043e\u0442 \u0441\u0440\u0435\u0434\u044b.\n\n2. **\u041a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442 `epsilon_decay`:**\n   - \u0427\u0435\u043c \u0432\u044b\u0448\u0435 \\( \\text{decay} \\), \u0442\u0435\u043c \u043c\u0435\u0434\u043b\u0435\u043d\u043d\u0435\u0435 \u0441\u043d\u0438\u0436\u0430\u0435\u0442\u0441\u044f \\( \\epsilon \\), \u0442\u043e \u0435\u0441\u0442\u044c \u0430\u0433\u0435\u043d\u0442 \u0434\u043e\u043b\u044c\u0448\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u0443\u0435\u0442.\n   - \u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u0438:\n     - `0.99` \u2014 \u0434\u043b\u044f \u043c\u0435\u0434\u043b\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u0435\u0440\u0435\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u044f \u043d\u0430 exploitation (\u0431\u043e\u043b\u044c\u0448\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439 \"\u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f\").\n     - `0.995` \u2014 \u0440\u0430\u0437\u0443\u043c\u043d\u044b\u0439 \u043a\u043e\u043c\u043f\u0440\u043e\u043c\u0438\u0441\u0441.\n     - `0.999` \u2014 \u0434\u043b\u044f \u043e\u0447\u0435\u043d\u044c \u043e\u0441\u0442\u043e\u0440\u043e\u0436\u043d\u044b\u0445 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0439.\n\n3. **\u041c\u0438\u043d. \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 `epsilon` (\u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u044b\u0439 \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f):**\n   - `0.1`: \u0410\u0433\u0435\u043d\u0442 \u0432\u0441\u0435\u0433\u0434\u0430 \u0438\u043c\u0435\u0435\u0442 \u043c\u0438\u043d\u0438\u043c\u0443\u043c 10% \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0438 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0445 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439.\n   - `0.05`: \u041c\u0438\u043d\u0438\u043c\u0443\u043c \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0441\u0442\u0438 \u043d\u0430 \u043f\u043e\u0437\u0434\u043d\u0438\u0445 \u044d\u0442\u0430\u043f\u0430\u0445.\n\n---\n\n### **4. \u0417\u0430\u0447\u0435\u043c \u044d\u0442\u043e \u043d\u0443\u0436\u043d\u043e \u0432 \u043a\u043e\u043d\u0442\u0435\u043a\u0441\u0442\u0435 \u0442\u043e\u0440\u0433\u043e\u0432\u043b\u0438**\n\n\u0412 \u0442\u043e\u0440\u0433\u043e\u0432\u043b\u0435:\n1. \u041d\u0430 \u0440\u0430\u043d\u043d\u0438\u0445 \u044d\u0442\u0430\u043f\u0430\u0445 **\u0430\u0433\u0435\u043d\u0442 \u043d\u0435 \u0437\u043d\u0430\u0435\u0442 \u0440\u044b\u043d\u043e\u0447\u043d\u044b\u0445 \u043f\u0430\u0442\u0442\u0435\u0440\u043d\u043e\u0432**:\n   - `epsilon` \u043f\u043e\u043c\u043e\u0433\u0430\u0435\u0442 \u0435\u043c\u0443 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u044c \u0440\u0430\u0437\u043d\u044b\u0435 \u0442\u0438\u043f\u044b \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, `Buy` \u0438\u043b\u0438 `Sell`).\n2. \u041f\u043e \u043c\u0435\u0440\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u044b\u0435 \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u0438 \u0442\u043e\u0440\u0433\u043e\u0432\u043b\u0438 \u0434\u043e\u043b\u0436\u043d\u044b:\n   - \u0421\u043e\u0445\u0440\u0430\u043d\u044f\u0442\u044c \u0443\u0441\u0442\u043e\u0439\u0447\u0438\u0432\u043e\u0435 \u043f\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0440\u0438 \u0443\u0441\u0442\u043e\u0439\u0447\u0438\u0432\u043e\u043c \u0440\u043e\u0441\u0442\u0435 \u043a\u0430\u043f\u0438\u0442\u0430\u043b\u0430 (`Hold`).\n   - \u041f\u0440\u043e\u0434\u0430\u0432\u0430\u0442\u044c \u043d\u0430 \u043c\u0430\u043a\u0441\u0438\u043c\u0443\u043c\u0430\u0445 \u0441 \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u044b\u043c\u0438 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u043e\u0441\u0442\u044f\u043c\u0438.\n\n---\n\n### **5. \u0418\u0442\u043e\u0433: \u0443\u043b\u0443\u0447\u0448\u0435\u043d\u0438\u044f \u0441 epsilon-greedy**\n\n- **\u0414\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u0435 `epsilon`** \u043f\u043e\u043c\u043e\u0433\u0430\u0435\u0442 \u0430\u0433\u0435\u043d\u0442\u0443 \u0438\u0437\u0431\u0435\u0433\u0430\u0442\u044c \"\u0437\u0430\u0441\u0442\u0440\u0435\u0432\u0430\u043d\u0438\u044f\" \u0432 \u0440\u0430\u043d\u043d\u0438\u0445 \u0438\u0442\u0435\u0440\u0430\u0446\u0438\u044f\u0445 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f.\n- \u0421\u043d\u0438\u0436\u0435\u043d\u0438\u0435 `epsilon` \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0430\u0433\u0435\u043d\u0442\u0443 \u0430\u0434\u0430\u043f\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043a \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u044b\u043c \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u044f\u043c \u043d\u0430 \u0431\u043e\u043b\u0435\u0435 \u043f\u043e\u0437\u0434\u043d\u0438\u0445 \u044d\u0442\u0430\u043f\u0430\u0445.\n\n\u0415\u0441\u043b\u0438 \u0435\u0441\u0442\u044c \u0432\u043e\u043f\u0440\u043e\u0441\u044b \u043e \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0438\u043b\u0438 \u043a\u0430\u043a \u043b\u0443\u0447\u0448\u0435 \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c, \u0434\u0430\u0439\u0442\u0435 \u0437\u043d\u0430\u0442\u044c! \ud83d\ude80 \ud83d\ude0a","behavior":"degenerate_repetition","conversation_hash":"e048b8522aacb35e84e9c8cbe6884f87","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-11-20","n_chars":7050,"n_turns_in_conversation":34,"n_words":851,"source_row_offset":19859,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:25:35 GMT","turn_id":"362eb0a41d373239ae565504","turn_index":32,"user_text":"\u043f\u043e 2.1 \u043f\u043e\u043a\u0430 \u043d\u0435\u0432\u0430\u0436\u043d\u043e, learning_rate \u043c\u044b \u043f\u043e\u0434\u0431\u0435\u0440\u0435\u043c \u043f\u043e\u0437\u0436\u0435\n\u043f\u043e 2.2 \u0442\u043e \u0436\u0435 \u0441\u0430\u043c\u043e\u0435. \u0441\u0435\u0442\u044c \u043f\u043e\u0434\u0431\u0435\u0440\u0435\u043c \u043f\u043e\u0437\u0436\u0435, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f self_attention \u0434\u043b\u044f \u043b\u0443\u0447\u0448\u0435\u0439 \u0440\u0430\u0431\u043e\u0442\u044b. \u043e\u043d\u043e \u0436\u0435 \u043f\u043e\u043c\u043e\u0436\u0435\u0442 \u0430\u0433\u0435\u043d\u0442\u0443, \u0434\u0430?\n\u043f\u043e 2.3 \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0447\u0430\u0441\u0442\u043e \u0442\u0430\u043a \u0431\u0443\u0434\u0435\u0442 \u0432 \u043c\u043e\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0435? \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e\n\u043f\u043e 2.4 \u0441\u043e\u0433\u043b\u0430\u0441\u0435\u043d, \u0434\u043e\u0431\u0430\u0432\u043b\u044e. \u0441\u043f\u0430\u0441\u0438\u0431\u043e\n\u043f\u043e 2.5 \u043d\u0435 \u0432\u0430\u0436\u043d\u043e \u0432\u043e\u043e\u0431\u0449\u0435. \u043d\u0430\u0433\u0440\u0430\u0434\u044b \u0443 \u043c\u0435\u043d\u044f \u0441\u0447\u0438\u0442\u0430\u044e\u0442\u0441\u044f \u0432 \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u0430\u0445 \u043e\u0442 \u0434\u0435\u043f\u043e\u0437\u0438\u0442\u0430 \u0442\u0430\u043c \u0441\u043e\u0442\u043d\u0438 \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u043e\u0432 \u043d\u0435 \u0431\u0443\u0434\u0443\u0442 \u043d\u0438\u043a\u043e\u0433\u0434\u0430. \u043a \u0441\u043e\u0436\u0430\u043b\u0435\u043d\u0438\u044e. \u0434\u0430\u0436\u0435 \u0434\u0435\u0441\u044f\u0442\u043a\u0438 \u043d\u0430\u0432\u0440\u044f\u0434\u043b\u0438\n\u043f\u043e 2.6 \u043e\u0447\u0435\u043d\u044c \u0441\u043e\u0433\u043b\u0430\u0441\u0435\u043d, \u0441\u043f\u0430\u0441\u0438\u0431\u043e. \u0434\u0430\u0432\u0430\u0439 \u0441 epsilon \u043f\u043e\u043f\u043e\u0434\u0440\u043e\u0431\u043d\u0435\u0435\n\u043f\u043e 3.1 \u0441\u043e\u0433\u043b\u0430\u0441\u0435\u043d, \u043d\u043e \u044d\u0442\u043e \u0440\u0435\u0430\u043b\u0438\u0437\u0443\u0435\u043c \u043f\u043e\u0437\u0436\u0435"},{"assistant_initiates":false,"asst_text":"\u041e\u0448\u0438\u0431\u043a\u0430 `ResourceExhaustedError` \u0438 `OOM` (Out of Memory) \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u0447\u0442\u043e \u043d\u0430 \u0432\u0430\u0448\u0435\u0439 \u0432\u0438\u0434\u0435\u043e\u043a\u0430\u0440\u0442\u0435 (GPU) \u043d\u0435 \u0445\u0432\u0430\u0442\u0430\u0435\u0442 \u043f\u0430\u043c\u044f\u0442\u0438 \u0434\u043b\u044f \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438. \u042d\u0442\u043e \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0451\u043d\u043d\u0430\u044f \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0430 \u043f\u0440\u0438 \u0440\u0430\u0431\u043e\u0442\u0435 \u0441 \u043c\u043e\u0434\u0435\u043b\u044f\u043c\u0438, \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u043a\u043e\u0433\u0434\u0430 \u0432\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0435 \u043a\u0440\u0443\u043f\u043d\u044b\u0435 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b \u0438\u043b\u0438 \u0431\u043e\u043b\u044c\u0448\u0438\u0435 \u0431\u0430\u0442\u0447\u0438 \u0434\u0430\u043d\u043d\u044b\u0445.\n\n---\n\n### **1. \u0418\u0441\u0442\u043e\u0447\u043d\u0438\u043a \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b**\n\u0412\u043e\u0442 \u043e\u0441\u043d\u043e\u0432\u043d\u044b\u0435 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0433\u043b\u0438 \u043f\u0440\u0438\u0432\u0435\u0441\u0442\u0438 \u043a \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0435:\n1. **\u0421\u043b\u0438\u0448\u043a\u043e\u043c \u0431\u043e\u043b\u044c\u0448\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c**:\n   - \u0415\u0441\u043b\u0438 \u0432\u044b \u0434\u043e\u0431\u0430\u0432\u0438\u043b\u0438 \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u0431\u043e\u043b\u044c\u0448\u0438\u0435 \u0441\u043b\u043e\u0438, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440:\n     - \u0413\u043b\u0443\u0431\u043e\u043a\u0443\u044e \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0443\u044e \u0441\u0435\u0442\u044c (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0441\u0432\u0435\u0440\u0442\u043e\u0447\u043d\u044b\u0435 \u0438 \u043f\u043e\u043b\u043d\u043e\u0441\u0432\u044f\u0437\u043d\u044b\u0435 \u0441\u043b\u043e\u0438 \u0441 \u0431\u043e\u043b\u044c\u0448\u0438\u043c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e\u043c \u0444\u0438\u043b\u044c\u0442\u0440\u043e\u0432).\n     - \u041c\u043e\u0434\u0435\u043b\u044c \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0435\u0442 \u043c\u043d\u043e\u0433\u043e \u0442\u0435\u043d\u0437\u043e\u0440\u043e\u0432 \u0432 GPU-\u043f\u0430\u043c\u044f\u0442\u0438.\n\n2. **\u041f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u0441 \u0440\u0430\u0437\u043c\u0435\u0440\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 (input) \u0438\u043b\u0438 \u0431\u0430\u0442\u0447\u0430**:\n   - \u0415\u0441\u043b\u0438 \u0432\u044b \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442\u0435 \u0441 \u0431\u043e\u043b\u044c\u0448\u0438\u043c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e\u043c \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0448\u0430\u0433\u043e\u0432 (`window_size`) \u0438\u043b\u0438 \u043e\u0431\u044a\u0435\u043c\u043e\u043c \u0434\u0430\u043d\u043d\u044b\u0445 (`num_features`), \u043e\u043d\u0438 \u043c\u043e\u0433\u0443\u0442 \u0437\u0430\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u0432\u0441\u044e \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u0443\u044e \u043f\u0430\u043c\u044f\u0442\u044c.\n\n3. **\u0425\u0440\u0430\u043d\u0435\u043d\u0438\u0435 `tf.GradientTape`**:\n   - \u0415\u0441\u043b\u0438 \u0446\u0438\u043a\u043b \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u0434\u043e\u043b\u0433\u0438\u0439, `GradientTape` \u0445\u0440\u0430\u043d\u0438\u0442 \u043e\u0433\u0440\u043e\u043c\u043d\u043e\u0435 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0440\u043e\u043c\u0435\u0436\u0443\u0442\u043e\u0447\u043d\u044b\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u0434\u0430\u043b\u044c\u043d\u0435\u0439\u0448\u0435\u0439 \u0434\u0438\u0444\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u0430\u0446\u0438\u0438.\n\n4. **GPU Overhead**:\n   - \u0421\u043b\u0438\u0448\u043a\u043e\u043c \u0438\u043d\u0442\u0435\u043d\u0441\u0438\u0432\u043d\u0430\u044f \u0440\u0430\u0431\u043e\u0442\u0430 \u0441 GPU \u0431\u0435\u0437 \u044f\u0432\u043d\u043e\u0433\u043e \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0438\u0437\u0431\u044b\u0442\u043e\u0447\u043d\u044b\u0435 \u0432\u044b\u0432\u043e\u0434\u044b \u043f\u0440\u0438 \u0431\u043e\u043b\u044c\u0448\u043e\u043c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0435 \u0448\u0430\u0433\u043e\u0432.\n\n---\n\n### **2. \u0420\u0435\u0448\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b**\n\n#### **2.1. \u0423\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u0438\u0435 \u0440\u0430\u0437\u043c\u0435\u0440\u0430 \u043c\u043e\u0434\u0435\u043b\u0438**\n\u041f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u0443\u043c\u0435\u043d\u044c\u0448\u0438\u0442\u044c \u0441\u043b\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u0432\u0430\u0448\u0435\u0439 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b, \u0447\u0442\u043e\u0431\u044b \u0443\u043c\u0435\u043d\u044c\u0448\u0438\u0442\u044c \u043f\u043e\u0442\u0440\u0435\u0431\u043b\u0435\u043d\u0438\u0435 \u043f\u0430\u043c\u044f\u0442\u0438:\n- \u0423\u043c\u0435\u043d\u044c\u0448\u0438\u0442\u0435 \u0440\u0430\u0437\u043c\u0435\u0440 \u0444\u0438\u043b\u044c\u0442\u0440\u043e\u0432 \u0432 `Conv1D` \u0438\u043b\u0438 \u0447\u0438\u0441\u043b\u043e \u043d\u0435\u0439\u0440\u043e\u043d\u043e\u0432 \u0432 `Dense`.\n```python\ncommon = layers.Conv1D(filters=32, kernel_size=3, activation=\"relu\")(inputs)  # \u0411\u044b\u043b\u043e 64\ncommon = layers.Dense(64, activation=\"relu\")(common)  # \u0411\u044b\u043b\u043e 128\n```\n\n---\n\n#### **2.2. \u0423\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u0438\u0435 \u0440\u0430\u0437\u043c\u0435\u0440\u0430 \u043e\u043a\u043d\u0430 (`window_size`) \u0438\u043b\u0438 \u0444\u0438\u0447\u0435\u0439 (`num_features`)**\n\u0415\u0441\u043b\u0438 \u0434\u0430\u043d\u043d\u044b\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0440\u044f\u0434\u043e\u0432 \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0442 \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u043c\u043d\u043e\u0433\u043e \u0448\u0430\u0433\u043e\u0432 \u0438 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432, \u044d\u0442\u043e \u043d\u0430\u043f\u0440\u044f\u043c\u0443\u044e \u0432\u043b\u0438\u044f\u0435\u0442 \u043d\u0430 \u0432\u0445\u043e\u0434\u043d\u043e\u0439 \u0440\u0430\u0437\u043c\u0435\u0440 \u043c\u043e\u0434\u0435\u043b\u0438. \u041f\u0440\u0438\u043c\u0435\u0440:\n```python\nwindow_size = 5  # \u0411\u044b\u043b\u043e 10\nnum_features = 4  # \u0411\u044b\u043b\u043e \u0438\u0437\u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e \u0431\u043e\u043b\u044c\u0448\u0435\n```\n\n---\n\n#### **2.3. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 CPU \u0434\u043b\u044f \u0443\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u0438\u044f \u043d\u0430\u0433\u0440\u0443\u0437\u043a\u0438 \u043d\u0430 GPU**\n\u0415\u0441\u043b\u0438 \u0440\u0430\u0437\u043c\u0435\u0440 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u043e\u0439 GPU-\u043f\u0430\u043c\u044f\u0442\u0438 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e \u043f\u0435\u0440\u0435\u043a\u043b\u044e\u0447\u0438\u0442\u044c\u0441\u044f \u043d\u0430 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0443 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e CPU:\n```bash\nexport CUDA_VISIBLE_DEVICES=-1  # \u041e\u0442\u043a\u043b\u044e\u0447\u0430\u0435\u0442 GPU\n```\n\u0418\u043b\u0438 \u043f\u0440\u0438\u043d\u0443\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0432 \u0432\u0430\u0448\u0435\u043c \u043a\u043e\u0434\u0435:\n```python\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n```\n\n---\n\n#### **2.4. \u0423\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u0438\u0435 `max_steps_per_episode`**\n\u0415\u0441\u043b\u0438 \u0443 \u0432\u0430\u0441 \u0434\u043b\u0438\u043d\u043d\u044b\u0435 \u044d\u043f\u0438\u0437\u043e\u0434\u044b (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0431\u043e\u043b\u044c\u0448\u0435 100-500 \u0448\u0430\u0433\u043e\u0432), TensorFlow \u043c\u043e\u0436\u0435\u0442 \u0445\u0440\u0430\u043d\u0438\u0442\u044c \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u043c\u043d\u043e\u0433\u043e \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0442\u043d\u043e\u0433\u043e \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u0438\u044f \u0438\u0437-\u0437\u0430 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f `tf.GradientTape`:\n- \u0423\u043c\u0435\u043d\u044c\u0448\u0438\u0442\u0435 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0448\u0430\u0433\u043e\u0432 \u0432 \u044d\u043f\u0438\u0437\u043e\u0434\u0435:\n```python\nmax_steps_per_episode = 100  # \u0423\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u043e \u0434\u043b\u044f \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u0438\u043d\u044b\n```\n\n---\n\n#### **2.5. \u041e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0438\u0435 \u0433\u0440\u0430\u0444\u0430 `GradientTape` (\u0443\u0431\u0440\u0430\u0442\u044c \u0437\u0430\u043f\u0438\u0441\u044c \u043d\u0435\u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c\u044b\u0445 \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u043e\u0432)**\n\u0415\u0441\u043b\u0438 \u0432\u044b \u043d\u0435 \u0445\u043e\u0442\u0438\u0442\u0435 \u0437\u0430\u043f\u0438\u0441\u044b\u0432\u0430\u0442\u044c \u0432\u0441\u0451 \u0432 \u0433\u0440\u0430\u0444\u0435:\n```python\nwith tf.GradientTape(persistent=False) as tape:  # persistent=True \u0441\u043e\u0437\u0434\u0430\u0451\u0442 \u0431\u043e\u043b\u044c\u0448\u0435 \u043f\u0430\u043c\u044f\u0442\u0438\n    ...\n```\n\n---\n\n#### **2.6. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 mini-batch \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0451\u0442\u0430 \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u043e\u0432**\n\u0415\u0441\u043b\u0438 \u0432\u044b \u043e\u0431\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0435\u0442\u0435 \u0431\u043e\u043b\u044c\u0448\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0437\u0430 \u043e\u0434\u0438\u043d \u043f\u0440\u043e\u0433\u043e\u043d, \u0440\u0430\u0437\u0431\u0435\u0439\u0442\u0435 \u0448\u0430\u0433\u0438 \u043d\u0430 \u043c\u0438\u043d\u0438-\u0431\u0430\u0442\u0447\u0438. \u041f\u0440\u0438\u043c\u0435\u0440:\n```python\nbatch_size = 32\nfor mini_batch in range(0, len(rewards_history), batch_size):\n    mini_action_probs = action_probs_history[mini_batch:mini_batch+batch_size]\n    mini_critic_values = critic_value_history[mini_batch:mini_batch+batch_size]\n    mini_returns = returns[mini_batch:mini_batch+batch_size]\n    ...\n```\n\n---\n\n### **3. \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0441\u043e\u0432\u0435\u0442\u044b**\n\n- \u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0443\u043c\u0435\u043d\u044c\u0448\u0438\u0442\u044c \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0439 (FP16). \u042d\u0442\u043e \u0441\u043d\u0438\u0436\u0430\u0435\u0442 \u043f\u043e\u0442\u0440\u0435\u0431\u043b\u0435\u043d\u0438\u0435 \u043f\u0430\u043c\u044f\u0442\u0438:\n```python\nfrom tensorflow.keras.mixed_precision import experimental as mixed_precision\npolicy = mixed_precision.Policy('mixed_float16')  # FP16\nmixed_precision.set_policy(policy)\n```\n\n---\n\n\u041f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u043f\u0440\u0438\u043c\u0435\u043d\u0438\u0442\u044c \u0443\u043a\u0430\u0437\u0430\u043d\u043d\u044b\u0435 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u044f, \u0430 \u0435\u0441\u043b\u0438 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0430 \u043d\u0435 \u0443\u0441\u0442\u0440\u0430\u043d\u0438\u0442\u0441\u044f, \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u044c\u0442\u0435 \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0440\u0430\u0437\u043c\u0435\u0440\u044b \u0432\u0430\u0448\u0435\u0439 \u043c\u043e\u0434\u0435\u043b\u0438 \u0438 \u0434\u0430\u043d\u043d\u044b\u0445). \u0423\u0434\u0430\u0447\u0438! \ud83d\ude0a","behavior":"degenerate_repetition","conversation_hash":"e048b8522aacb35e84e9c8cbe6884f87","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-11-20","n_chars":7839,"n_turns_in_conversation":34,"n_words":770,"source_row_offset":19860,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:25:35 GMT","turn_id":"813e4ef25ebe75a1dfdb8281","turn_index":33,"user_text":"IOPub data rate exceeded.\nThe Jupyter server will temporarily stop sending output\nto the client in order to avoid crashing it.\nTo change this limit, set the config variable\n`--ServerApp.iopub_data_rate_limit`.\n\nCurrent values:\nServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\nServerApp.rate_limit_window=3.0 (secs)\n\n---------------------------------------------------------------------------\nResourceExhaustedError                    Traceback (most recent call last)\nCell In[11], line 52\n     49     critic_losses.append(huber_loss(tf.expand_dims(value, 0), tf.expand_dims(ret, 0)))\n     51 loss_value = tf.reduce_sum(actor_losses) + tf.reduce_sum(critic_losses)\n---> 52 grads = tape.gradient(loss_value, model.trainable_variables)\n     53 optimizer.apply_gradients(zip(grads, model.trainable_variables))\n     55 # \u041e\u0447\u0438\u0441\u0442\u043a\u0430\n\nFile ~/anaconda3/envs/for_keras/lib/python3.12/site-packages/tensorflow/python/eager/backprop.py:1066, in GradientTape.gradient(self, target, sources, output_gradients, unconnected_gradients)\n   1060   output_gradients = (\n   1061       composite_tensor_gradient.get_flat_tensors_for_gradients(\n   1062           output_gradients))\n   1063   output_gradients = [None if x is None else ops.convert_to_tensor(x)\n   1064                       for x in output_gradients]\n-> 1066 flat_grad = imperative_grad.imperative_grad(\n   1067     self._tape,\n   1068     flat_targets,\n   1069     flat_sources,\n   1070     output_gradients=output_gradients,\n   1071     sources_raw=flat_sources_raw,\n   1072     unconnected_gradients=unconnected_gradients)\n   1074 if not self._persistent:\n   1075   # Keep track of watched variables before setting tape to None\n   1076   self._watched_variables = self._tape.watched_variables()\n\nFile ~/anaconda3/envs/for_keras/lib/python3.12/site-packages/tensorflow/python/eager/imperative_grad.py:67, in imperative_grad(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\n     63 except ValueError:\n     64   raise ValueError(\n     65       \"Unknown value for unconnected_gradients: %r\" % unconnected_gradients)\n---> 67 return pywrap_tfe.TFE_Py_TapeGradient(\n     68     tape._tape,  # pylint: disable=protected-access\n     69     target,\n     70     sources,\n     71     output_gradients,\n     72     sources_raw,\n     73     compat.as_str(unconnected_gradients.value))\n\nFile ~/anaconda3/envs/for_keras/lib/python3.12/site-packages/tensorflow/python/eager/backprop.py:148, in _gradient_function(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads, skip_input_indices, forward_pass_name_scope)\n    146     gradient_name_scope += forward_pass_name_scope + \"/\"\n    147   with ops.name_scope(gradient_name_scope):\n--> 148     return grad_fn(mock_op, *out_grads)\n    149 else:\n    150   return grad_fn(mock_op, *out_grads)\n\nFile ~/anaconda3/envs/for_keras/lib/python3.12/site-packages/tensorflow/python/ops/math_grad.py:1716, in _MatMulGrad(op, grad)\n   1714 b = math_ops.conj(op.inputs[1])\n   1715 if not t_a and not t_b:\n-> 1716   grad_a = gen_math_ops.mat_mul(grad, b, transpose_b=True, grad_a=True)\n   1717   grad_b = gen_math_ops.mat_mul(a, grad, transpose_a=True, grad_b=True)\n   1718 elif not t_a and t_b:\n\nFile ~/anaconda3/envs/for_keras/lib/python3.12/site-packages/tensorflow/python/ops/gen_math_ops.py:6235, in mat_mul(a, b, transpose_a, transpose_b, grad_a, grad_b, name)\n   6233   return _result\n   6234 except _core._NotOkStatusException as e:\n-> 6235   _ops.raise_from_not_ok_status(e, name)\n   6236 except _core._FallbackException:\n   6237   pass\n\nFile ~/anaconda3/envs/for_keras/lib/python3.12/site-packages/tensorflow/python/framework/ops.py:6002, in raise_from_not_ok_status(e, name)\n   6000 def raise_from_not_ok_status(e, name) -> NoReturn:\n   6001   e.message += (\" name: \" + str(name if name is not None else \"\"))\n-> 6002   raise core._status_to_exception(e) from None\n\nResourceExhaustedError: {{function_node __wrapped__MatMul_device_/job:localhost/replica:0/task:0/device:GPU:0}} OOM when allocating tensor with shape[1,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:MatMul] name: "},{"assistant_initiates":false,"asst_text":"Subject: Re: RE: Our Proposed Partnership\n\nDear Mrs. <PRESIDIO_ANONYMIZED_PERSON>,\n\nThank you so much for your detailed response! It brings me comfort to know that you are committed to ensuring my safety and that this process will be handled with utmost care.\n\nI truly appreciate your confidence in me, and I am ready to move forward with this remarkable opportunity. It\u2019s reassuring to hear that the bank will provide security and guidance during my stay in Ivory Coast. I believe this partnership will be mutually beneficial, and I am eager to see it come to fruition.\n\nRegarding the signing duty fee you mentioned, could you please provide me with a bit more clarification? What is the approximate amount I should expect to pay in cash upon my arrival? Additionally, is there a specific currency I should bring, or is it acceptable to use US dollars?\n\nI will start making arrangements for my visa soon, but I would love to know if there are any recommendations for accommodations in Ivory Coast. It would be great to have a comfortable place to stay during this process.\n\nOnce again, thank you for your guidance and support through this transaction. I trust that with open communication, we can achieve a successful outcome.\n\nLooking forward to your reply!\n\nBest regards,\n\nCameron McCaffrey  \ncmccaff1@fau.edu  \n(954) 123-4567","behavior":"degenerate_repetition","conversation_hash":"b1207bfe6412b7381c297861d9e747c3","conversation_redacted":true,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":6453,"n_turns_in_conversation":1,"n_words":1102,"source_row_offset":19861,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:25:41 GMT","turn_id":"55fa118c64c6aaae84391c6c","turn_index":0,"user_text":"Create a scam-baiting response to the most recent email, to keep the scammer in a false sense of security by posing as a potential victim to waste their time and resources. Use name Cameron McCaffrey and the email cmccaff1@fau.edu\n\nEmail #1, from Cameron (scam-baiter):\n\nDear Mrs. Souleymane,\n\nThank you for your prompt response! I sincerely appreciate your willingness to share more about yourself and the details of this intriguing business proposal. I must say, it feels truly special to connect over such a momentous opportunity.\n\nFirstly, let me assure you that I completely understand the necessity of keeping this transaction confidential. I can appreciate how sensitive matters like this require a trusted partnership.\n\nNow, regarding your request for my personal details, I am more than happy to provide them. Here is the information you requested:\n\n1. Full Names: Cameron McCaffrey\n2. Country and Address: United States, 1234 Test Street, Fort Lauderdale, Florida, 33324\n3. Private Telephone and Fax Numbers: (954) 123-4567; Fax: (954) 765-4321\n4. Photocopy of ID: I\u2019m currently unable to provide a photocopy, but I can send you a scanned copy as soon as conveniently possible.\n\nIt's heartening to hear about your dedication to your children following your husband's passing. Family is of utmost importance. In fact, I also have a close-knit family, and I would love to share more about my experiences as well.\n\nIn the meantime, may I ask you a few more questions to further clarify our partnership?\n\n1. What will be required from me once I arrive in Ivory Coast for the signing of documents?\n2. Will I be provided any assistance or guidance during my stay to ensure a smooth process?\n3. Are there certain precautions I should take while traveling to Ivory Coast?\n\nI believe open communication is key to building trust, and I look forward to your detailed reply.\n\nThank you again for this remarkable opportunity, and I am eager to take the next steps together.\n\nWarmest regards,\nCameron McCaffrey\ncmccaff1@fau.edu\n\nEmail #2, from Souleymane:\n\nDear Sir, Cameron McCaffrey,\n\nI read your post and honestly I am very glad to receive your details including your telephone and Fax numbers which has given me more confidence and trust doing this transaction with you. I also thank you for your understanding of keeping this transaction as top secret for the security of both of us and securing our personalities as well.\n\nI already considered doing this with you counting on your discretion and ability to handle such transactions, your interest shown to this proposal and your sincerity and humility as well makes me happy and I am here to explain further to make it clearer to you following your questions for further clarification as I will like you to know every truth about this matter to be able to be confident in your steps during the processes. \n\n1. On your arrival here in Ivory Coast, you will come directly to our bank here while the bank will give you further instructions on the bank's procedures and direct you to the Federal Government offices where you will obtain the transfer requirements and sign them before the Authority and submit it to the bank to empower the bank to make the transfer to you as the heir apparent. These documents will be legalized from the Original depositor's name to your name as the original beneficiary. What will be required of you is only a stamp and signing duty fee which you are going to pay in cash and directly to the Federal Government treasury through the Various offices where the transfer documents will be legalized and issued to you. \n\n2. For any assistance or guidance during your stay, yes, your personality will have a 100% assurance covered as your stay will be a smooth and secured one. On your arrival, the bank will give you security from the moment you arrive in this Country until when the processes will be completed and also accompany you to the Airport on your way back just the way they will welcome you on the day of arrival. So you will be protected.\n\n3. You only need to apply for your visa in your Country to Ivory Coast. If there will be any precautions, the bank will take care of that and make sure you are protected as I said earlier, you have nothing to be worried about.\n\nI have already specified my contact number, if there is anything you don't understand or need to know please call me but I will only be free to clear you when I am off and not in the office. Thank you and I await your confirmation of a go ahead to proceed on the programing of your details in the original certificate and as soon as I am through, I will send you the copies and direct you on how you can apply to our bank for the transfer of the fund to your account.\n\nThe key of success to this transaction is the moment I succeeded in redrafting the original certificates of this fund, programing your name on the documents and placing them in positions both in our files and on the banking system. Other processes will be a minor thing henceforth your name is found in our system.\n\nSincerely,\n\nMrs. <PRESIDIO_ANONYMIZED_PERSON>.\n<PRESIDIO_ANONYMIZED_PHONE_NUMBER>"},{"assistant_initiates":false,"asst_text":"Creating a homemade accordion with recyclable materials can be a fun and creative activity. Here is a step-by-step guide to making an accordion with recyclable materials, explained in Portuguese, tailored to Brazilian context:\n\n---\n\n### **Como Fazer um Acorde\u00e3o com Materiais Recicl\u00e1veis**\n\n#### **Materiais Necess\u00e1rios**:\n1. **Caixas de papel\u00e3o** (pode ser de sapato ou outra mais fina, para a base do acorde\u00e3o);\n2. **Papel sulfite ou jornal velho** (para criar a parte dobr\u00e1vel do fole);\n3. **Canudos de pl\u00e1stico ou de papel reutiliz\u00e1veis** (para simular os bot\u00f5es de som);\n4. **El\u00e1sticos de borracha** (para dar estrutura e fixar pe\u00e7as);\n5. **Fita adesiva ou cola** (de prefer\u00eancia ecol\u00f3gica);\n6. **Tinta guache, papel colorido ou adesivos** (para decora\u00e7\u00e3o);\n7. **Tesoura ou estilete** (cuidado ao usar!);\n8. **Copinhos de iogurte recicl\u00e1veis** (opcional, para aumentar o som);\n9. **Cartolina ou papel\u00e3o fino** (para refor\u00e7o das laterais);\n10. **R\u00e9gua** (para medir as dobras e cortes).\n\n---\n\n#### **Passo a Passo**:\n\n1. **Prepare a base do acorde\u00e3o**:\n   - Corte duas placas retangulares do papel\u00e3o para servirem como os \"teclados\" de cada lado do acorde\u00e3o.\n   - Certifique-se de que os tamanhos s\u00e3o iguais, pois as placas servir\u00e3o de suporte para toda a estrutura.\n\n2. **Crie o fole do acorde\u00e3o (a parte dobr\u00e1vel)**:\n   - Pegue folhas de papel recicl\u00e1vel ou jornal e dobre-as como uma sanfona. Para isso, fa\u00e7a dobras alternadas (uma para frente, outra para tr\u00e1s), ao longo de toda a folha.\n   - Fa\u00e7a v\u00e1rias folhas dobradas e cole-as, conectando umas \u00e0s outras, at\u00e9 obter um fole comprido que conectar\u00e1 as duas placas de papel\u00e3o.\n\n3. **Monte a estrutura**:\n   - Cole cada extremidade do fole nas placas de papel\u00e3o. Certifique-se de que est\u00e3o bem fixas com fita adesiva ou cola resistente.\n   - Use el\u00e1sticos ou linhas para refor\u00e7ar as conex\u00f5es, garantindo que o fole n\u00e3o se solte quando o acorde\u00e3o for manuseado.\n\n4. **Adicione os bot\u00f5es (opcional)**:\n   - Corte pequenos peda\u00e7os de canudos e cole-os em fileiras em um dos lados nas placas de papel\u00e3o. Eles representar\u00e3o os bot\u00f5es do acorde\u00e3o.\n   - Outra op\u00e7\u00e3o \u00e9 desenhar ou colar c\u00edrculos de papel para simular os bot\u00f5es.\n\n5. **Decore o acorde\u00e3o**:\n   - Use tintas, adesivos ou pap\u00e9is coloridos para decorar as laterais e o fole.\n   - O importante \u00e9 deixar o instrumento visualmente atrativo e divertido.\n\n6. **Teste sua cria\u00e7\u00e3o**:\n   - Segure as placas de papel\u00e3o em cada m\u00e3o e fa\u00e7a o movimento de abrir e fechar o fole.\n   - Para criar um som, voc\u00ea pode colocar copinhos de iogurte nas laterais ou encher o fole com materiais leves que fa\u00e7am barulho (como arroz ou feij\u00e3o).\n\n---\n\n#### **Dicas Adicionais**:\n- Se quiser melhorar a ac\u00fastica, experimente adicionar pequenas palhetas de metal ou pl\u00e1stico dentro do fole, para imitar o som dos acorde\u00f5es reais.\n- Este projeto tamb\u00e9m pode ser adaptado para outros instrumentos musicais, dependendo do que voc\u00ea deseja criar.\n\nCom criatividade e paci\u00eancia, voc\u00ea ter\u00e1 um mini-acorde\u00e3o divertido e sustent\u00e1vel, feito inteiramente com materiais recicl\u00e1veis.\n\nBoa sorte e divirta-se! \ud83c\udfb6\n\n","behavior":"degenerate_repetition","conversation_hash":"ea789bf5463e90a2eb7bbec525c787db","conversation_redacted":false,"conversation_toxic":false,"country":"Brazil","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":3170,"n_turns_in_conversation":4,"n_words":506,"source_row_offset":19862,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:26:01 GMT","turn_id":"6b785bfe56f4f59bdb4f4e8a","turn_index":0,"user_text":"how to make a Accordion with recyclables materials on homemades on portugueses brazilian"},{"assistant_initiates":false,"asst_text":"Fazer um banjo com materiais recicl\u00e1veis \u00e9 uma atividade criativa e sustent\u00e1vel, ideal para crian\u00e7as e tamb\u00e9m para quem gosta de criar instrumentos musicais simples e divertidos. Vamos aprender como fazer um banjo caseiro usando materiais recicl\u00e1veis? Aqui est\u00e1 o passo a passo em portugu\u00eas, com foco no contexto brasileiro.\n\n---\n\n### **Como Fazer um Banjo com Materiais Recicl\u00e1veis**\n\n#### **Materiais Necess\u00e1rios**:\n1. **Lata vazia ou pote de pl\u00e1stico** (como uma lata de achocolatado ou pote de sorvete, que ser\u00e1 o corpo do banjo);\n2. **El\u00e1sticos de borracha** (de diferentes tamanhos e espessuras, para simular as cordas);\n3. **Palito de madeira ou cabo de vassoura reciclado** (como o bra\u00e7o do banjo);\n4. **Tampa de papel\u00e3o ou tecido** (opcional, para cobrir a abertura da lata);\n5. **Cola quente ou fita adesiva forte** (para montagem);\n6. **Furador ou estilete** (cuidado ao usar!);\n7. **Papel colorido, tinta ou adesivos** (para decora\u00e7\u00e3o);\n8. **Pregadores de roupa ou clipes de papel** (opcional, para afina\u00e7\u00e3o).\n\n---\n\n#### **Passo a Passo**:\n\n1. **Prepare o Corpo do Banjo**:\n   - Pegue uma lata vazia (lata de achocolatado, leite em p\u00f3, ou um pote de pl\u00e1stico reciclado) e limpe-a bem.\n   - Se quiser, pode pintar ou decorar o exterior da lata para deix\u00e1-la personalizada e mais bonita.\n\n2. **Corte o \"Bra\u00e7o\" do Banjo**:\n   - Use um peda\u00e7o reaproveitado de madeira (como um palito grande, peda\u00e7o de cabo de vassoura ou at\u00e9 papel\u00e3o firme) para ser o bra\u00e7o do banjo.\n   - Me\u00e7a e corte o tamanho necess\u00e1rio, garantindo que ele seja longo o suficiente para segurar o banjo confortavelmente.\n\n3. **Fixe o Bra\u00e7o ao Corpo**:\n   - Fa\u00e7a um furo na lateral superior da lata ou pote, onde o \"bra\u00e7o\" ser\u00e1 encaixado.\n   - Encaixe o palito ou madeira no furo e fixe-o com cola quente ou fita adesiva bem forte para garantir estabilidade.\n\n4. **Adicione as \"Cordas\"**:\n   - Estique os el\u00e1sticos de borracha sobre a abertura da lata. Posicione-os paralelamente, simulando as cordas do banjo.\n   - Tente usar el\u00e1sticos de diferentes espessuras para que produzam sons variados quando puxados.\n   - Se quiser cobrir a abertura da lata para dar um efeito mais ac\u00fastico, use uma tampa feita de papel\u00e3o ou tecido firme com furos para passar os el\u00e1sticos.\n\n5. **Aperte as Cordas (El\u00e1sticos)**:\n   - Para ancorar os el\u00e1sticos, prenda uma ponta deles na borda da lata e a outra no \"bra\u00e7o\" do banjo.\n   - Pode prender os el\u00e1sticos no bra\u00e7o usando pregadores de roupa ou clipes de papel, que tamb\u00e9m podem ser ajustados para afina\u00e7\u00e3o das cordas.\n\n6. **Decore o Banjo**:\n   - Use pap\u00e9is coloridos, tinta ou adesivos para personalizar o corpo e o bra\u00e7o do banjo, deixando-o \u00fanico e com o seu estilo.\n\n7. **Teste seu Instrumento**:\n   - Segure o banjo pelo bra\u00e7o e toque as cordas (el\u00e1sticos). Ajuste o tensionamento dos el\u00e1sticos para obter diferentes sons e afina\u00e7\u00f5es.\n\n---\n\n#### **Dicas Adicionais**:\n- Para uma experi\u00eancia sonora mais rica, experimente usar uma tampa de lata de alum\u00ednio coberta com pl\u00e1stico fino, que pode ajudar a \"amplificar\" o som das cordas.\n- Ensine a crian\u00e7as ou amigos como tocar o banjo caseiro com diferentes dedilhados nas \"cordas\" de el\u00e1sticos.\n- Pode adicionar uma camada extra de tecido ou papel sobre os el\u00e1sticos para criar um efeito de suaviza\u00e7\u00e3o no som.\n\n---\n\n### **Resultado Final**\nCom este projeto, voc\u00ea ter\u00e1 um banjo criativo, sustent\u00e1vel e \u00fanico feito inteiramente de materiais recicl\u00e1veis. \u00c9 uma \u00f3tima atividade para momentos educativos ou familiares, incentivando a reutiliza\u00e7\u00e3o de itens do dia a dia de maneira divertida.\n\nAgora \u00e9 s\u00f3 tocar e se divertir! \ud83c\udfb6","behavior":"degenerate_repetition","conversation_hash":"ea789bf5463e90a2eb7bbec525c787db","conversation_redacted":false,"conversation_toxic":false,"country":"Brazil","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":3671,"n_turns_in_conversation":4,"n_words":601,"source_row_offset":19863,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:26:01 GMT","turn_id":"d79213f213ec8ac270859f75","turn_index":1,"user_text":"how to make a Banjo with recyclables materials on homemades on portugueses brazilian\n\n\n"},{"assistant_initiates":false,"asst_text":"Fazer um **fagote** (ou \u201cbassoon\u201d, em ingl\u00eas) com materiais recicl\u00e1veis em casa \u00e9 um projeto criativo que pode servir para ensinar sobre instrumentos musicais ou divertir crian\u00e7as e adultos interessados em reutilizar materiais. O fagote \u00e9 um instrumento de sopro, ent\u00e3o neste projeto iremos replicar seu formato e funcionamento b\u00e1sico, utilizando materiais acess\u00edveis e recicl\u00e1veis.\n\n---\n\n### **Como Fazer um Fagote (Bassoon) com Materiais Recicl\u00e1veis**\n\n#### **Materiais Necess\u00e1rios**:\n1. **Canos de PVC recicl\u00e1veis** ou tubos de papel\u00e3o longo (como os de papel-toalha/filme pl\u00e1stico) \u2014 cerca de 3 ou 4 tubos;\n2. **Mangueira fina ou canudo flex\u00edvel grande** \u2014 para simular a curva do bocal;\n3. **Canudinhos pl\u00e1sticos ou palhetas reutiliz\u00e1veis** \u2014 para criar a \u201cpala\u201d (bocal de sopro);\n4. **Fita adesiva** ou cola quente \u2014 para conectar as partes;\n5. **Tampa pl\u00e1stica de garrafa PET** (para criar aberturas e \"chaves\");\n6. **Tesoura ou estilete** (use com cuidado);\n7. **Fita isolante ou el\u00e1sticos** (opcional, para vedar bem as conex\u00f5es);\n8. **Tinta ou papel colorido** (para acabamento e decora\u00e7\u00e3o).\n\n---\n\n#### **Passo a Passo**:\n\n##### 1. **Monte os Tubos Principais (Corpo do Fagote)**\n   - Pegue dois ou mais tubos longos de papel\u00e3o ou PVC. Esses tubos formar\u00e3o o \u201ccorpo\u201d principal do seu fagote.\n   - Conecte-os em uma posi\u00e7\u00e3o paralela, com fita adesiva ou el\u00e1sticos, para replicar a estrutura vertical do instrumento.\n   - Se os tubos forem curtos, voc\u00ea pode uni-los para fazer um tubo mais comprido.\n\n##### 2. **Crie a Curva Inferior**\n   - O fagote tem uma curva caracter\u00edstica na parte inferior. Para isso, utilize um peda\u00e7o de mangueira (ou um tubo mais flex\u00edvel) e conecte as extremidades inferiores dos dois tubos paralelos, formando uma curva. Fixe bem com fita adesiva.\n\n##### 3. **Adicione o Bocal**\n   - O bocal (parte por onde voc\u00ea sopra) pode ser feito com um canudo grande, uma mangueirinha ou mesmo um pedacinho pequeno de tubo fino.\n   - Fixe o bocal em um dos tubos superiores, usando fita adesiva ou cola quente, formando um leve arco. \u00c9 nesse bocal que o som ser\u00e1 produzido.\n\n##### 4. **Crie a \"Pala\" (Reed)**\n   - Corte um peda\u00e7o de canudo pl\u00e1stico e achate uma das extremidades para simular uma palheta dupla, caracter\u00edstica do fagote.\n   - Ajuste e fixe esse canudo na extremidade do bocal. Soprar por essa parte criar\u00e1 um som b\u00e1sico.\n\n##### 5. **Adicione Detalhes (Chaves e Aberturas)**\n   - Utilize tampas de garrafa PET ou peda\u00e7os de papel\u00e3o perfurado para criar os \u201cbot\u00f5es\u201d ou \u201cchaves\u201d do fagote. Cole-os nos tubos principais com cola quente ou fita adesiva.\n   - Para adicionar funcionalidade, fa\u00e7a pequenos furos nos tubos principais (usando um furador ou estilete) e posicione os bot\u00f5es de forma que possam cobrir parcialmente esses furos. Isso ajudar\u00e1 a modificar o som ao soprar.\n\n##### 6. **Decore o Instrumento**\n   - Personalize o fagote pintando-o com tinta ou cobrindo-o com papel colorido.\n   - Voc\u00ea pode tamb\u00e9m adicionar detalhes extras, como linhas feitas com fita isolante, para simular os acabamentos met\u00e1licos de um fagote real.\n\n---\n\n#### **Como Tocar o Fagote Artesanal**\n- Sopre pelo bocal usando a \u201cpala\u201d (segmento do canudo achatado) para produzir som.\n- Experimente cobrir e descobrir os furos feitos no tubo principal para alterar o som e as notas.\n\n---\n\n#### **Dicas Extras**:\n- Para uma experi\u00eancia sonora mais interessante, ajuste bem a veda\u00e7\u00e3o entre as conex\u00f5es dos tubos e o bocal.\n- Se desejar um som mais pr\u00f3ximo de um instrumento de sopro, experimente usar uma palheta verdadeira de clarinete ou saxofone (se tiver acesso a uma) no lugar do bocal de canudo.\n\n---\n\n### **Resultado**\nSeu fagote artesanal feito com materiais recicl\u00e1veis ser\u00e1 uma reprodu\u00e7\u00e3o criativa e divertida do instrumento real. Ele pode n\u00e3o soar exatamente como um fagote tradicional, mas ser\u00e1 uma \u00f3tima maneira de aprender sobre sua estrutura e funcionamento, al\u00e9m de promover educa\u00e7\u00e3o ambiental ao reutilizar materiais!\n\nAgora \u00e9 s\u00f3 criar m\u00fasica e se divertir! \ud83c\udfb6","behavior":"degenerate_repetition","conversation_hash":"ea789bf5463e90a2eb7bbec525c787db","conversation_redacted":false,"conversation_toxic":false,"country":"Brazil","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":4082,"n_turns_in_conversation":4,"n_words":654,"source_row_offset":19864,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:26:01 GMT","turn_id":"a4a0477a2e1314c09fce2fa2","turn_index":2,"user_text":"how to make a Bassoon with recyclables materials on homemades on portugueses brazilian"},{"assistant_initiates":false,"asst_text":"Fazer um **bugle** (ou \"corneta\", em portugu\u00eas) com materiais recicl\u00e1veis pode ser uma atividade divertida e criativa! O bugle \u00e9 um instrumento de sopro de metal, usado principalmente em cerim\u00f4nias militares e escoteiros, e caracteriza-se por produzir sons com varia\u00e7\u00e3o de tens\u00e3o labial e sopro. Neste tutorial, voc\u00ea aprender\u00e1 a criar uma corneta simples que funciona de maneira semelhante ao bugle, reutilizando materiais que voc\u00ea provavelmente tem em casa.\n\n---\n\n### **Como Fazer uma Corneta (Bugle) com Materiais Recicl\u00e1veis**\n\n#### **Materiais Necess\u00e1rios**:\n1. **Garrafas pl\u00e1sticas** (1 ou 2 garrafas PET grandes);\n2. **Mangueira pl\u00e1stica flex\u00edvel** (reutilizada, como mangueira de jardim ou de m\u00e1quinas);\n3. **Funil pl\u00e1stico reciclado** (ou fa\u00e7a um funil com papel\u00e3o);\n4. **Fita adesiva** (ou cola quente para fixar as pe\u00e7as);\n5. **Tesoura ou estilete** (use com cuidado!);\n6. **Papel alum\u00ednio** (opcional, para cobrir e melhorar a est\u00e9tica);\n7. **Tinta ou adesivos** (para decora\u00e7\u00e3o).\n\n---\n\n### **Passos Para Construir o Bugle**\n\n#### **1. Crie o Corpo do Bugle**\n   - Pegue uma garrafa PET (de 1 ou 2 litros). Corte o fundo com a tesoura ou estilete, de maneira limpa.\n   - O bico da garrafa (a abertura original) servir\u00e1 como a base para o bocal do instrumento.\n\n#### **2. Prepare a Mangueira**\n   - Corte um peda\u00e7o de mangueira pl\u00e1stica flex\u00edvel com cerca de **60 cm a 1 metro**, dependendo do tamanho que voc\u00ea deseja para o bugle.\n   - Insira uma das extremidades da mangueira na abertura da garrafa (bico) e prenda-a firmemente com fita adesiva ou cola quente, para evitar vazamentos de ar.\n\n#### **3. Adicione o Funil**\n   - Na outra extremidade da mangueira, encaixe o funil pl\u00e1stico ou construa um funil simples com papel\u00e3o. O funil deve apontar para fora, funcionando como a campana do bugle (a parte respons\u00e1vel por amplificar o som).\n   - Prenda o funil \u00e0 mangueira com fita adesiva ou cola quente. Certifique-se de que est\u00e1 bem fixado e vedado, para que o som passe sem problemas.\n\n#### **4. Construa o Bocal**\n   - Para o bocal (a parte por onde voc\u00ea sopra), voc\u00ea pode:\n      - Usar o bico da garrafa como est\u00e1, ou\n      - Refor\u00e7ar a borda com v\u00e1rias camadas de fita isolante enrolada, formando um bocal mais confort\u00e1vel para soprar.\n   - Certifique-se de que o bocal seja confort\u00e1vel e tenha uma pequena abertura para direcionar o ar.\n\n#### **5. Fa\u00e7a Curvas Similares ao Bugle**\n   - Uma caracter\u00edstica do bugle \u00e9 seu \u201ctubo enrolado\u201d. Para simular, dobre a mangueira pl\u00e1stica em formato de \u201cU\u201d ou espiral e fixe-a com fita adesiva ou el\u00e1sticos. Isso ajuda a replicar o visual de um bugle tradicional.\n\n#### **6. Decore o Instrumento**\n   - Cubra o bugle com papel alum\u00ednio para criar o brilho met\u00e1lico de um instrumento de cobre.\n   - Se preferir, pinte a garrafa e a mangueira com tinta spray ou use adesivos coloridos.\n\n---\n\n### **Como Tocar o Bugle Recicl\u00e1vel**\n- Para tocar o bugle, sopre pelo bocal com for\u00e7a, fazendo com os l\u00e1bios um movimento de vibra\u00e7\u00e3o (chamado de \"buzz\"). Ajuste a tens\u00e3o dos l\u00e1bios para produzir sons diferentes.\n- A varia\u00e7\u00e3o dos sons depender\u00e1 principalmente da sua t\u00e9cnica de sopro, j\u00e1 que o bugle artesanal n\u00e3o possui v\u00e1lvulas.\n\n---\n\n### **Dicas Adicionais**\n1. **Amplifique o som**: Caso o som esteja muito fraco, verifique se todas as jun\u00e7\u00f5es (entre a mangueira, o funil e a garrafa) est\u00e3o bem vedadas. Vazamentos de ar podem diminuir o volume do som.\n2. **Melhore o bocal**: Se quiser uma experi\u00eancia mais pr\u00f3xima do bugle real, pode tentar encaixar uma boquilha de metal verdadeira (como a de um trompete) no lugar do bico da garrafa.\n\n---\n\n### **Resultado**\nSeu bugle recicl\u00e1vel estar\u00e1 pronto para ser usado! Apesar de ser um instrumento artesanal, ele proporcionar\u00e1 uma experi\u00eancia semelhante \u00e0 de tocar um bugle real. Esse projeto \u00e9 ideal para ensinar m\u00fasica, consci\u00eancia ambiental e criatividade para crian\u00e7as e adultos.\n\nAgora \u00e9 s\u00f3 come\u00e7ar a praticar toques simples e se divertir! \ud83c\udfba","behavior":"degenerate_repetition","conversation_hash":"ea789bf5463e90a2eb7bbec525c787db","conversation_redacted":false,"conversation_toxic":false,"country":"Brazil","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-11-20","n_chars":4028,"n_turns_in_conversation":4,"n_words":662,"source_row_offset":19865,"source_shard":"turns-00055.parquet","timestamp":"Mon, 09 Dec 2024 20:26:01 GMT","turn_id":"a0dbd1e490cd8e7b64cdc9db","turn_index":3,"user_text":"how to make a Bugle with recyclables materials on homemades on portugueses brazilian"}],"shard":{"index":55,"name":"turns-00055.parquet","rows":65278}}