{"id":31371,"date":"2024-10-28T12:01:34","date_gmt":"2024-10-28T12:01:34","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=31371"},"modified":"2024-11-26T06:45:22","modified_gmt":"2024-11-26T06:45:22","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d%ea%b3%bc-%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d%ec%9d%84-%ed%99%9c%ec%9a%a9%ed%95%9c-%ec%9e%90%eb%8f%99%eb%a7%a4%eb%a7%a4-%ea%b0%95%ed%99%94%ed%95%99%ec%8a%b5-%ed%99%98","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/31371\/","title":{"rendered":"\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \uac15\ud654\ud559\uc2b5 \ud658\uacbd \uad6c\uc131 \ubc0f \uc5d0\uc774\uc804\ud2b8 \ud559\uc2b5 OpenAI Gym\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac70\ub798 \ud658\uacbd \uad6c\uc131\uacfc \uac15\ud654\ud559\uc2b5 \ud559\uc2b5 \uacfc\uc815."},"content":{"rendered":"
\n

\n \uc624\ub298\ub0a0 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uacfc \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc740 \uc8fc\uc694\ud55c \uc8fc\uc81c\uac00 \ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc2e0\uc18d\ud55c \uacb0\uc815\uacfc \uc2e4\ud589\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uace0, OpenAI Gym\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uac15\ud654\ud559\uc2b5 \ud658\uacbd\uc744 \uad6c\uc131\ud558\ub294 \ubc29\ubc95 \ubc0f \uc5d0\uc774\uc804\ud2b8\ub97c \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1. \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4\uc758 \ud544\uc694\uc131<\/h2>\n

\n \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4\ub294 \uac70\ub798\uc790\uac00 \uc2dc\uc7a5\uc5d0 \ub300\ud55c \ubd84\uc11d\uc744 \ud1b5\ud574 \uc989\uac01\uc801\uc778 \ub9e4\ub9e4 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud569\ub2c8\ub2e4. \uc0ac\ub78c\uc758 \uac10\uc815\uc744 \ubc30\uc81c\ud558\uace0, \uc54c\uace0\ub9ac\uc998\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud568\uc73c\ub85c\uc368 \ub354 \ub098\uc740 \uac70\ub798 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucd5c\uadfc\uc5d0\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubc95\uc774 \uc774 \ubd84\uc57c\uc5d0 \uc801\uc6a9\ub418\uc5b4 \ub354\uc6b1 \uc815\uad50\ud55c \uc608\uce21 \ubaa8\ub378\uc774 \uac1c\ubc1c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

2. \uac15\ud654\ud559\uc2b5(Deep Reinforcement Learning)\uc5d0 \ub300\ud55c \uc774\ud574<\/h2>\n

\n \uac15\ud654\ud559\uc2b5\uc740 \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uc5ec \ucd5c\uc801\uc758 \uc758\uc0ac\uacb0\uc815\uc744 \ud559\uc2b5\ud558\ub294 \uae30\uacc4 \ud559\uc2b5 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ubcf4\uc0c1 \uc2e0\ud638\ub97c \ubc1b\uc73c\uba70 \ud589\ub3d9\uc744 \uc870\uc815\ud558\uace0, \ucd5c\uc801\uc758 \uc815\ucc45\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \ube44\ud2b8\ucf54\uc778 \ub9e4\ub9e4\uc5d0\uc11c\ub294 \uac00\uaca9 \ubcc0\ub3d9\uc774\ub098 \ub2e4\ub978 \uc2dc\uc7a5 \uc9c0\ud45c\ub4e4\uc744 \uae30\ubc18\uc73c\ub85c \ub9e4\uc218, \ub9e4\ub3c4 \ub610\ub294 \ub300\uae30\uc640 \uac19\uc740 \ud589\ub3d9\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4.\n <\/p>\n

3. OpenAI Gym\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac70\ub798 \ud658\uacbd \uad6c\uc131<\/h2>\n

\n OpenAI Gym\uc740 \ub2e4\uc591\ud55c \uac15\ud654\ud559\uc2b5 \ud658\uacbd\uc744 \uc81c\uacf5\ud558\ub294 \ud234\ud0b7\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ube44\ud2b8\ucf54\uc778 \uac70\ub798 \ud658\uacbd\uc744 \uc124\uc815\ud560 \uc218 \uc788\uc73c\uba70, \uc5d0\uc774\uc804\ud2b8\ub294 \uc774 \ud658\uacbd \ub0b4\uc5d0\uc11c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. OpenAI Gym\uc744 \uc0ac\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \ud2b8\ub808\uc774\ub529 \ud658\uacbd\uc744 \ub9cc\ub4e4\uae30 \uc704\ud574 \ud544\uc694\ud55c \uc694\uc18c\ub4e4\uc744 \ub2e4\uc74c\uacfc \uac19\uc774 \uc815\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

    \n
  1. \n\ud658\uacbd \uad6c\ucd95:<\/strong> \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uc5ec Gym \ud658\uacbd\uc744 \uad6c\uc131\ud569\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \uc5d0\uc774\uc804\ud2b8\uc758 \uc0c1\ud0dc\ub97c \uc815\uc758\ud558\uace0 \ubcf4\uc0c1 \uad6c\uc870\ub97c \uc124\uacc4\ud569\ub2c8\ub2e4.\n <\/li>\n
  2. \n\ud589\ub3d9 \uc815\uc758:<\/strong> \ub9e4\uc218, \ub9e4\ub3c4, \ub300\uae30\uc640 \uac19\uc740 \ud589\ub3d9\uc744 \uc815\uc758\ud558\uc5ec \uac01 \uc0c1\ud0dc\uc5d0\uc11c \uc5d0\uc774\uc804\ud2b8\uac00 \uc120\ud0dd\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.\n <\/li>\n
  3. \n\ubcf4\uc0c1 \uad6c\uc870 \uc124\uacc4:<\/strong> \uc5d0\uc774\uc804\ud2b8\uc758 \ud589\ub3d9\uc5d0 \ub530\ub77c \uc5bb\ub294 \ubcf4\uc0c1\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc218\uc775\uc774 \ubc1c\uc0dd\ud558\uba74 \uae0d\uc815\uc801\uc778 \ubcf4\uc0c1\uc744 \uc8fc\uace0, \uc190\uc2e4\uc774 \ubc1c\uc0dd\ud558\uba74 \ubd80\uc815\uc801\uc778 \ubcf4\uc0c1\uc744 \uc8fc\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4.\n <\/li>\n<\/ol>\n

    3.1. \uc608\uc81c\ucf54\ub4dc: \ube44\ud2b8\ucf54\uc778 \uac70\ub798 \ud658\uacbd<\/h3>\n
    \n    \n    import numpy as np\n    import gym\n    from gym import spaces\n\n    class BitcoinTradingEnv(gym.Env):\n        def __init__(self, data):\n            super(BitcoinTradingEnv, self).__init__()\n            self.data = data\n            self.current_step = 0\n            \n            # \uc561\uc158 \uacf5\uac04 \uc815\uc758: 0 - \ub300\uae30, 1 - \ub9e4\uc218, 2 - \ub9e4\ub3c4\n            self.action_space = spaces.Discrete(3)\n            \n            # \uc0c1\ud0dc \uacf5\uac04 \uc815\uc758: \ud604\uc7ac \uc790\uc0b0, \ubcf4\uc720\ub7c9, \uac00\uaca9\n            self.observation_space = spaces.Box(low=0, high=np.inf, shape=(3,), dtype=np.float32)\n\n        def reset(self):\n            self.current_step = 0\n            self.balance = 1000  # \ucd08\uae30 \uc790\uc0b0\n            self.holding = 0      # \ubcf4\uc720 \ube44\ud2b8\ucf54\uc778\n            return self._get_observation()\n\n        def _get_observation(self):\n            price = self.data[self.current_step]\n            return np.array([self.balance, self.holding, price])\n\n        def step(self, action):\n            current_price = self.data[self.current_step]\n            reward = 0\n\n            if action == 1:  # \ub9e4\uc218\n                if self.balance >= current_price:\n                    self.holding += 1\n                    self.balance -= current_price\n                    reward = -1  # \ube44\uc6a9: \ub9e4\uc218\n            elif action == 2:  # \ub9e4\ub3c4\n                if self.holding > 0:\n                    self.holding -= 1\n                    self.balance += current_price\n                    reward = 1  # \uc218\uc775: \ub9e4\ub3c4\n\n            self.current_step += 1\n            done = self.current_step >= len(self.data)\n            return self._get_observation(), reward, done, {}\n\n    # \uc0ac\uc6a9 \uc608\uc2dc\n    data = np.random.rand(100) * 100  # \uac00\uc0c1\uc758 \uac00\uaca9 \ub370\uc774\ud130\n    env = BitcoinTradingEnv(data)\n    <\/code>\n    <\/pre>\n
    4. \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc774\uc6a9\ud55c \uc5d0\uc774\uc804\ud2b8 \ud559\uc2b5<\/h2>\n
    \n        \uac15\ud654\ud559\uc2b5 \uc5d0\uc774\uc804\ud2b8\ub97c \ud6c8\ub828\ud558\ub824\uba74 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc801\uc6a9\ud558\uc5ec \uc815\ucc45\uc774\ub098 \uac00\uce58\ub97c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 DQN(Deep Q-Network) \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. DQN\uc740 Q-learning \uc54c\uace0\ub9ac\uc998\uc744 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc5d0 \ud1b5\ud569\ud55c \uac83\uc73c\ub85c, \uc0c1\ud0dc\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 Q\uac12\uc744 \ucd9c\ub825\ud569\ub2c8\ub2e4.\n    <\/p>\n
    4.1. \uc608\uc81c\ucf54\ub4dc: DQN \uc54c\uace0\ub9ac\uc998<\/h3>\n
    \n    \n    import numpy as np\n    import tensorflow as tf\n    from collections import deque\n\n    class DQNAgent:\n        def __init__(self, action_size):\n            self.action_size = action_size\n            self.state_size = 3\n            self.memory = deque(maxlen=2000)\n            self.gamma = 0.95  # \ud560\uc778\uc728\n            self.epsilon = 1.0  # \ud0d0\uc0c9\uc728\n            self.epsilon_min = 0.01\n            self.epsilon_decay = 0.995\n            self.model = self._build_model()\n\n        def _build_model(self):\n            model = tf.keras.Sequential()\n            model.add(tf.keras.layers.Dense(24, input_dim=self.state_size, activation='relu'))\n            model.add(tf.keras.layers.Dense(24, activation='relu'))\n            model.add(tf.keras.layers.Dense(self.action_size, activation='linear'))\n            model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=0.001))\n            return model\n\n        def remember(self, state, action, reward, next_state, done):\n            self.memory.append((state, action, reward, next_state, done))\n\n        def act(self, state):\n            if np.random.rand() <= self.epsilon:\n                return np.random.choice(self.action_size)\n            act_values = self.model.predict(state)\n            return np.argmax(act_values[0])\n\n        def replay(self, batch_size):\n            minibatch = np.random.choice(len(self.memory), batch_size)\n            for index in minibatch:\n                state, action, reward, next_state, done = self.memory[index]\n                target = reward\n                if not done:\n                    target += self.gamma * np.amax(self.model.predict(next_state)[0])\n                target_f = self.model.predict(state)\n                target_f[0][action] = target\n                self.model.fit(state, target_f, epochs=1, verbose=0)\n            if self.epsilon > self.epsilon_min:\n                self.epsilon *= self.epsilon_decay\n\n    # \uc0ac\uc6a9 \uc608\uc2dc\n    agent = DQNAgent(action_size=3)\n    <\/code>\n    <\/pre>\n
    4.2. \uc5d0\uc774\uc804\ud2b8 \ud559\uc2b5 \uacfc\uc815<\/h3>\n
    \n        \uc5d0\uc774\uc804\ud2b8\ub294 \uc5ec\ub7ec \uc5d0\ud53c\uc18c\ub4dc\ub97c \ud1b5\ud574 \ubc18\uc9c0\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4. \uac01 \uc5d0\ud53c\uc18c\ub4dc\ub9c8\ub2e4 \ud658\uacbd\uc744 \ucd08\uae30\ud654\ud558\uace0, \uc5d0\uc774\uc804\ud2b8\uc758 \ud589\ub3d9\uc744 \uae30\ubc18\uc73c\ub85c \uc0c1\ud0dc, \ubcf4\uc0c1 \ubc0f \ub2e4\uc74c \uc0c1\ud0dc\ub97c \uc5bb\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc815\ubcf4\ub97c \uae30\uc5b5\ud558\uace0, \uc9c0\uc815\ub41c \ubc30\uce58 \ud06c\uae30\ub9cc\ud07c \uc0d8\ud50c\ub9c1\ud558\uc5ec \ubaa8\ub378\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n
            \ub2e4\uc74c\uc740 \uc5d0\uc774\uc804\ud2b8\ub97c \ud6c8\ub828\uc2dc\ud0a4\uace0 \uc131\uacfc\ub97c \ud3c9\uac00\ud558\ub294 \uae30\ubcf8 \uad6c\uc870\uc785\ub2c8\ub2e4:\n    <\/p>\n
    \n    \n    episodes = 1000\n    batch_size = 32\n\n    for e in range(episodes):\n        state = env.reset()\n        state = np.reshape(state, [1, agent.state_size])\n        for time in range(500):\n            action = agent.act(state)\n            next_state, reward, done, _ = env.step(action)\n            next_state = np.reshape(next_state, [1, agent.state_size])\n            agent.remember(state, action, reward, next_state, done)\n            state = next_state\n            if done:\n                print(f'\uc5d0\ud53c\uc18c\ub4dc: {e}\/{episodes}, \uc810\uc218: {time}, epsilon: {agent.epsilon:.2}')\n                break\n            if len(agent.memory) > batch_size:\n                agent.replay(batch_size)\n    <\/code>\n    <\/pre>\n
    5. \uacb0\ub860<\/h2>\n
    \n        \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uacfc OpenAI Gym\uc744 \ud1b5\ud574 \uac15\ud654\ud559\uc2b5 \ud658\uacbd\uc744 \uad6c\uc131\ud558\uace0 \uc5d0\uc774\uc804\ud2b8\ub97c \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc744 \uc124\uba85\ud588\uc2b5\ub2c8\ub2e4. \ube44\ud2b8\ucf54\uc778 \ub9e4\ub9e4\uc5d0\uc11c \uac15\ud654\ud559\uc2b5\uc744 \uc801\uc6a9\ud558\ub294 \uac83\uc740 \uc5ec\uc804\ud788 \uc5f0\uad6c\uac00 \ub9ce\uc740 \ubd84\uc57c\uc774\uba70, \uc2e4\uc804\uc5d0\uc11c\uc758 \uc131\uacfc\ub97c \uc704\ud574 \ub2e4\uc591\ud55c \uc804\ub7b5\uacfc \uc811\uadfc\ubc95\uc744 \uc2e4\ud5d8\ud574 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
    \n        \uc55e\uc73c\ub85c \uc5ec\ub7ec\ubd84\uc758 \uc2dc\uc2a4\ud15c\uc774 \uc5b4\ub5bb\uac8c \ubc1c\uc804\ud560 \uc218 \uc788\uc744\uc9c0 \uae30\ub300\ub418\uba70, \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud1b5\ud574 \ubcf4\ub2e4 \uc2a4\ub9c8\ud2b8\ud55c \ud22c\uc790 \uacb0\uc815\uc744 \ub0b4\ub9ac\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.\n    <\/p>\n<\/article>\n","protected":false},"excerpt":{"rendered":"
    \uc624\ub298\ub0a0 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uacfc \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc740 \uc8fc\uc694\ud55c \uc8fc\uc81c\uac00 \ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc2e0\uc18d\ud55c \uacb0\uc815\uacfc \uc2e4\ud589\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uace0, OpenAI Gym\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uac15\ud654\ud559\uc2b5 \ud658\uacbd\uc744 \uad6c\uc131\ud558\ub294 \ubc29\ubc95 \ubc0f \uc5d0\uc774\uc804\ud2b8\ub97c \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4\uc758 \ud544\uc694\uc131 \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4\ub294 \uac70\ub798\uc790\uac00 \uc2dc\uc7a5\uc5d0 \ub300\ud55c \ubd84\uc11d\uc744 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \uac15\ud654\ud559\uc2b5 \ud658\uacbd \uad6c\uc131 \ubc0f \uc5d0\uc774\uc804\ud2b8 \ud559\uc2b5  OpenAI Gym\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac70\ub798 \ud658\uacbd \uad6c\uc131\uacfc \uac15\ud654\ud559\uc2b5 \ud559\uc2b5 \uacfc\uc815.”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-31371","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \uac15\ud654\ud559\uc2b5 \ud658\uacbd \uad6c\uc131 \ubc0f \uc5d0\uc774\uc804\ud2b8 \ud559\uc2b5 OpenAI Gym\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac70\ub798 \ud658\uacbd \uad6c\uc131\uacfc \uac15\ud654\ud559\uc2b5 \ud559\uc2b5 \uacfc\uc815. - 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