\uba54\ubaa8\ub9ac \uc0ac\uc6a9 \ucd5c\uc18c\ud654:<\/strong> \uac01 \uc5d0\uc774\uc804\ud2b8\uac00 \ub3c5\ub9bd\uc801\uc778 \ud30c\ub77c\ubbf8\ud130\ub97c \uac00\uc9c0\uace0 \uc788\ub294 \uac83\ubcf4\ub2e4 \uba54\ubaa8\ub9ac \uc0ac\uc6a9\uc744 \ucd5c\uc18c\ud654\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n3. \ubaa8\ub4c8\ud654 \ubc0f \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720 \uad6c\ud604 \ubc29\ubc95<\/h2>\n
\uc774\uc81c \ubaa8\ub4c8\ud654 \ubc0f \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\ub97c \uc5b4\ub5bb\uac8c \ud30c\uc774\uc36c\uc73c\ub85c \uad6c\ud604\ud560 \uc218 \uc788\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 Keras \ud150\uc11c\ud50c\ub85c\uc6b0\ub97c \uc0ac\uc6a9\ud558\uc5ec Deep Q-Network (DQN) \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\ub294 \uc608\uc81c\ub97c \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
3-1. \ud658\uacbd \uc124\uc815<\/h3>\n
\uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ud558\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
pip install gym tensorflow numpy<\/code><\/pre>\n3-2. DQN \uc5d0\uc774\uc804\ud2b8 \ubaa8\ub4c8\ud654<\/h3>\n
\uc5d0\uc774\uc804\ud2b8\uc758 \ubaa8\ub4c8\ud654\ub97c \uc704\ud574 \uba3c\uc800 Q-Network \ud074\ub798\uc2a4\ub97c \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ud074\ub798\uc2a4\ub294 Deep Learning \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uba70, \uc774\ub97c \ud1b5\ud574 \uc0c1\ud0dc\ub97c \uc785\ub825\ubc1b\uc544 Q \uac12\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/p>\n
\nimport numpy as np\nimport gym\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.optimizers import Adam\n\nclass DQNAgent:\n def __init__(self, state_size, action_size):\n self.state_size = state_size\n self.action_size = action_size\n self.memory = []\n self.gamma = 0.95 # discount rate\n self.epsilon = 1.0 # exploration rate\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 = Sequential()\n model.add(Dense(24, input_dim=self.state_size, activation='relu'))\n model.add(Dense(24, activation='relu'))\n model.add(Dense(self.action_size, activation='linear'))\n model.compile(loss='mse', optimizer=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(self.memory, batch_size)\n for state, action, reward, next_state, done in minibatch:\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 <\/code><\/pre>\n3-3. \ud658\uacbd \uad6c\ucd95<\/h3>\n
\uc774\uc81c OpenAI Gym\uc758 CartPole \ud658\uacbd\uc744 \uc124\uc815\ud574 \uc5d0\uc774\uc804\ud2b8\ub97c \ud559\uc2b5\uc2dc\ucf1c \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nif __name__ == \"__main__\":\n env = gym.make('CartPole-v1')\n state_size = env.observation_space.shape[0]\n action_size = env.action_space.n\n agent = DQNAgent(state_size, action_size)\n episodes = 1000\n for e in range(episodes):\n state = env.reset()\n state = np.reshape(state, [1, state_size])\n for time in range(500):\n action = agent.act(state)\n next_state, reward, done, _ = env.step(action)\n reward = reward if not done else -10\n next_state = np.reshape(next_state, [1, state_size])\n agent.remember(state, action, reward, next_state, done)\n state = next_state\n if done:\n print(\"episode: {}\/{}, score: {}\".format(e, episodes, time))\n break\n if len(agent.memory) > 32:\n agent.replay(32)\n <\/code><\/pre>\n3-4. \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720 \uad6c\ud604<\/h3>\n
\uc5ec\ub7ec \uc5d0\uc774\uc804\ud2b8\ub97c \uc0dd\uc131\ud558\uace0, \uadf8\ub4e4\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \uacf5\uc720\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\ub97c \uc704\ud574 \uc5d0\uc774\uc804\ud2b8 \ud074\ub798\uc2a4\uc5d0 \uacf5\uc720 \ud30c\ub77c\ubbf8\ud130\ub97c \ubc1b\uc744 \uc218 \uc788\ub294 \ubc29\ubc95\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.<\/p>\n
\nclass DQNAgent:\n def __init__(self, state_size, action_size, shared_model=None):\n self.state_size = state_size\n self.action_size = action_size\n self.memory = []\n self.gamma = 0.95 \n self.epsilon = 1.0 \n self.epsilon_min = 0.01\n self.epsilon_decay = 0.995\n \n # \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\ub97c \uc704\ud574 \ubaa8\ub378\uc774 \uc804\ub2ec\ub418\uba74 \uc0ac\uc6a9\n if shared_model is not None:\n self.model = shared_model\n else:\n self.model = self._build_model()\n <\/code><\/pre>\n4. \ubaa8\ub4c8\ud654 \ubc0f \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\uc758 \uc2e4\uc81c \uc0ac\ub840<\/h2>\n
\uc2e4\uc81c\ub85c \ud30c\uc774\uc36c\uc73c\ub85c DQN\uc744 \uad6c\ud604\ud560 \ub54c \ubaa8\ub4c8\ud654 \ubc0f \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\uc758 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uba74, \uac01 \uc5d0\uc774\uc804\ud2b8\ub4e4\uc774 \ub3d9\uc77c\ud55c Q-Network\ub97c \uacf5\uc720\ud558\uac8c \ub418\ubbc0\ub85c \uac01\uac01\uc758 \uc0c1\ud0dc\uc5d0\uc11c \ud559\uc2b5\ud558\ub294 \ud6a8\uc728\uc774 \ud06c\uac8c \uc99d\uac00\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uad6c\ud604 \uae30\ubc95\uc740 Multi-Agent Reinforcement Learning\uacfc \uac19\uc740 \ub354 \ud655\uc7a5\ub41c \uc601\uc5ed\uc5d0\uc11c\ub3c4 \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4-1. \ucf54\ub4dc \ucd5c\uc801\ud654<\/h3>\n
\uc2e4\uc81c \ud658\uacbd\uc5d0\uc11c \uc5d0\uc774\uc804\ud2b8\uc758 \ud559\uc2b5 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uac15\ud654\ud559\uc2b5\uc758 \uacbd\uc6b0, \uc5ec\ub7ec \uc5d0\uc774\uc804\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \uacf5\ub3d9 \ud559\uc2b5\uc744 \ud1b5\ud574 \ub354 \uc88b\uc740 \uc131\ub2a5\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uc5d0\uc774\uc804\ud2b8\ub97c \ud655\uc7a5\ud558\uc5ec \uc5ec\ub7ec \uc5d0\uc774\uc804\ud2b8\uac00 \uac19\uc774 \ud559\uc2b5\ud558\ub3c4\ub85d \ud558\ub294 \uc608\uc81c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
\nagents = [DQNAgent(state_size, action_size, agent.model) for _ in range(5)]\nfor e in range(episodes):\n states = [env.reset() for _ in range(5)]\n # \ubaa8\ub4e0 \uc5d0\uc774\uc804\ud2b8\uac00 \ub3c5\ub9bd\ub41c \uc0c1\ud0dc\uc5d0\uc11c \ud589\ub3d9\uc744 \uc120\ud0dd\ud558\uace0, \uadf8 \uacb0\uacfc\ub97c \ud569\uc0b0\n for time in range(500):\n actions = [agent.act(np.reshape(state, [1, state_size])) for agent, state in zip(agents, states)]\n next_states, rewards, dones, _ = zip(*[env.step(action) for action in actions])\n for agent, state, action, reward, next_state, done in zip(agents, states, actions, rewards, next_states, dones):\n agent.remember(state, action, reward, next_state, done)\n \n states = [np.reshape(next_state, [1, state_size]) for next_state in next_states]\n for i in range(5):\n if dones[i]:\n print(\"episode: {}\/{}, score: {}\".format(e, episodes, time))\n break\n if len(agent.memory) > 32:\n agent.replay(32)\n <\/code><\/pre>\n\uacb0\ub860<\/h2>\n
\ubaa8\ub4c8\ud654 \ubc0f \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\ub97c \ud1b5\ud574 \ubcf5\uc7a1\ud55c \uac15\ud654\ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \uad00\ub9ac\ud558\uace0, \ucf54\ub4dc\uc758 \uc7ac\uc0ac\uc6a9\uc131\uacfc \uc720\uc9c0 \ubcf4\uc218\uc131\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c \uc124\uba85\ud55c \ub0b4\uc6a9\uc744 \uae30\ubc18\uc73c\ub85c \ub354\uc6b1 \ubc1c\uc804\ub41c \uac15\ud654\ud559\uc2b5 \ud504\ub85c\uc81d\ud2b8\ub97c \uc9c4\ud589\ud560 \uc218 \uc788\uc744 \uac83\uc774\uba70, \uc774\ub860\uacfc \uac1c\ub150\uc744 \ud568\uaed8 \uc775\ud78c\ub2e4\uba74 \ubd84\uba85 \ub354 \ub098\uc740 \ucf54\ub4dc\ub97c \uc791\uc131\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\ubaa8\ub4c8\ud654\uc640 \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\ub294 \ub2e8\uc21c\ud788 \ucf54\ub4dc \uc791\uc131 \uc2dc\uc758 \ud3b8\ub9ac\ud568 \uc678\uc5d0\ub3c4, \uc2e4\uc81c \ubd84\uc11d \ubc0f \uc5f0\uad6c\uc5d0\uc11c\uc758 \uc2e4\uc6a9\uc131\uc774 \ub9e4\uc6b0 \ud06c\ub2e4\ub294 \uc810\uc744 \uc720\ub150\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. \uac15\ud654\ud559\uc2b5\uc758 \ubcf5\uc7a1\ud55c \uad6c\uc870\ub97c \ub354 \uc798 \uc774\ud574\ud558\uace0 \uad00\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \uc9c0\uc18d\uc801\uc778 \uc5f0\uad6c\ub97c \uc774\uc5b4\uac00\uae38 \uad8c\uc7a5\ud569\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ud30c\uc774\uc36c\uc73c\ub85c \uac15\ud654\ud559\uc2b5(Deep Reinforcement Learning, DRL) \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\ub294 \uacfc\uc815\uc5d0\uc11c \uac00\uc7a5 \uc5b4\ub824\uc6b4 \ubd80\ubd84 \uc911 \ud558\ub098\ub294 \ubcf5\uc7a1\uc131\uc744 \uad00\ub9ac\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ud2b9\ud788, \uc5ec\ub7ec \ud658\uacbd\uc5d0\uc11c \ud559\uc2b5\ud558\uac70\ub098 \uc720\uc0ac\ud55c \ud589\uc704\ub97c \ubc18\ubcf5\uc801\uc73c\ub85c \uc218\ud589\ud574\uc57c \ud560 \ub54c, \uadf8 \ubcf5\uc7a1\uc131\uc740 \uae30\ud558\uae09\uc218\uc801\uc73c\ub85c \uc99d\uac00\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c, \uc774\ub7ec\ud55c \ubcf5\uc7a1\uc131\uc744 \uad00\ub9ac\ud558\uae30 \uc704\ud55c \uae30\ubc95\uc73c\ub85c \ubaa8\ub4c8\ud654 \ubc0f \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\uac00 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \uac1c\ub150\uacfc \uad6c\ud604 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uace0, \uc608\uc81c\ub97c \ud1b5\ud574 \uc2e4\uc9c8\uc801\uc778 \uc774\ud574\ub97c … \ub354 \ubcf4\uae30 “27. \ubaa8\ub4c8\ud654 \ubc0f \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\ub85c \ubcf5\uc7a1\uc131 \uad00\ub9ac\ud558\uae30, \ubaa8\ub4c8\ud654\uc758 \uac1c\ub150\uacfc \uad6c\ud604 \ubc29\ubc95”<\/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":[213],"tags":[],"class_list":["post-40464","post","type-post","status-publish","format-standard","hentry","category-213"],"yoast_head":"\n27. \ubaa8\ub4c8\ud654 \ubc0f \ud30c\ub77c\ubbf8\ud130 \uacf5\uc720\ub85c \ubcf5\uc7a1\uc131 \uad00\ub9ac\ud558\uae30, \ubaa8\ub4c8\ud654\uc758 \uac1c\ub150\uacfc \uad6c\ud604 \ubc29\ubc95 - 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