\uac15\ud654\ud559\uc2b5(Reinforcement Learning, RL)\uc740 \uae30\uacc4 \ud559\uc2b5\uc758 \ud55c \ubd84\uc57c\ub85c, \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba74\uc11c \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\ub860\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144\uac04 \uac15\ud654\ud559\uc2b5 \uae30\uc220\uc740 \ub9ce\uc740 \ubc1c\uc804\uc744 \uc774\ub8e8\uc5c8\uc73c\uba70, \ub2e4\uc591\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \ub3c4\uad6c\uac00 \uc774\ub7ec\ud55c \uae30\uc220\uc744 \uc9c0\uc6d0\ud558\uae30 \uc704\ud574 \uac1c\ubc1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uac15\ud654\ud559\uc2b5\uc744 \uc704\ud55c \uc8fc\uc694 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \ub3c4\uad6c\ub97c \uc18c\uac1c\ud558\uace0, \uac01 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 \ud2b9\uc9d5\uacfc \uc0ac\uc6a9\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
OpenAI Gym\uc740 \uac15\ud654\ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc758 \uac1c\ubc1c\uacfc \ud3c9\uac00\ub97c \uc704\ud55c \ud234\ud0b7\uc785\ub2c8\ub2e4. \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ub2e4\uc591\ud55c \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n
import gym\n\n# \ud658\uacbd \uc0dd\uc131\nenv = gym.make('CartPole-v1')\n\n# \ud658\uacbd \ucd08\uae30\ud654\nstate = env.reset()\n\ndone = False\nwhile not done:\n # \ub79c\ub364\ud55c \ud589\ub3d9 \uc120\ud0dd\n action = env.action_space.sample()\n state, reward, done, info = env.step(action)\n env.render()\n\nenv.close()<\/code><\/pre>\n2. TensorFlow Agents<\/h2>\n
TensorFlow Agents\ub294 TensorFlow \uae30\ubc18\uc758 \uac15\ud654\ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub2e4\uc591\ud55c RL \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\uace0 \uc2e4\ud5d8\ud560 \uc218 \uc788\ub294 \ubaa8\ub4c8\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n
\ud2b9\uc9d5<\/h3>\n\n- \uc720\uc5f0\ud55c \uc124\uacc4: \ub2e4\uc591\ud55c \uc720\ud615\uc758 \uc54c\uace0\ub9ac\uc998\uc744 \uc27d\uac8c \uc815\uc758\ud558\uace0 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \ud150\uc11c\ud50c\ub85c\uc6b0\uc640\uc758 \ud1b5\ud569: TensorFlow\uc640 \uc644\ubcbd\ud558\uac8c \ud1b5\ud569\ub418\uc5b4 \ub370\uc774\ud130 \ud750\ub984\uc744 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \ub2e4\uc591\ud55c \uc608\uc81c \uc81c\uacf5: \ub2e4\uc591\ud55c \uc608\uc81c\ub97c \ud1b5\ud574 \uc54c\uace0\ub9ac\uc998\uc744 \uc27d\uac8c \uc774\ud574\ud558\uace0 \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n
\uc0ac\uc6a9\ubc95<\/h3>\nimport tensorflow as tf\nfrom tf_agents.environments import suite_gym\nfrom tf_agents.agents.dqn import dqn_agent\nfrom tf_agents.networks import q_network\nfrom tf_agents.utils import common\n\n# \ud658\uacbd \uc0dd\uc131\nenv = suite_gym.load('CartPole-v0')\n\n# \ub124\ud2b8\uc6cc\ud06c \uc0dd\uc131\nq_net = q_network.QNetwork(env.observation_spec(), env.action_spec())\n\n# DQN \uc5d0\uc774\uc804\ud2b8 \uc0dd\uc131\nagent = dqn_agent.DqnAgent(env.time_step_spec(),\n env.action_spec(),\n q_network=q_net,\n optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3),\n train_step_counter=tf.Variable(0))\nagent.initialize()<\/code><\/pre>\n3. Stable Baselines3<\/h2>\n
Stable Baselines3\ub294 \ub2e4\uc591\ud55c \uac15\ud654\ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc758 \uc548\uc815\uc801\uc778 \uad6c\ud604\uc744 \uc81c\uacf5\ud558\ub294 \ud30c\uc774\uc36c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc0ac\uc6a9\uc790\uc758 \ud559\uc2b5\uc744 \ub3d5\uae30 \uc704\ud574 \uac04\ub2e8\ud558\uace0 \uba85\ud655\ud55c API\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n
\ud2b9\uc9d5<\/h3>\n\n- \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998: A2C, PPO, DDPG \ub4f1 \uc5ec\ub7ec \uc54c\uace0\ub9ac\uc998\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4.<\/li>\n
- \uc26c\uc6b4 \uc0ac\uc6a9: \ub9e4\uc6b0 \uc9c1\uad00\uc801\uc778 API\ub97c \ud1b5\ud574 \uac04\ub2e8\ud558\uac8c \uac15\ud654\ud559\uc2b5 \uc5d0\uc774\uc804\ud2b8\ub97c \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- OpenAI Gym \ud1b5\ud569: Gym\uacfc \ud568\uaed8 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n
\uc0ac\uc6a9\ubc95<\/h3>\nfrom stable_baselines3 import PPO\n from stable_baselines3.common.envs import CartPoleEnv\n\n# \ud658\uacbd \uc0dd\uc131\n env = CartPoleEnv()\n\n# \uc5d0\uc774\uc804\ud2b8 \uc0dd\uc131\n model = PPO('MlpPolicy', env, verbose=1)\n \n# \ubaa8\ub378 \ud6c8\ub828\n model.learn(total_timesteps=10000)<\/code><\/pre>\n4. Ray RLLib<\/h2>\n
Ray RLLib\uc740 \ubd84\uc0b0 \ud658\uacbd\uc5d0\uc11c \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \uac15\ud654\ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \ub300\uaddc\ubaa8 \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \ub370 \ucd5c\uc801\ud654\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ud2b9\uc9d5<\/h3>\n\n- \ubd84\uc0b0 \ud559\uc2b5: \ud074\ub7ec\uc2a4\ud130\ub97c \uad6c\uc131\ud558\uace0 \ubd84\uc0b0 \ud559\uc2b5\uc744 \ud1b5\ud574 \ub300\uaddc\ubaa8 \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \ubaa8\ub4c8\ud654: RL \uc54c\uace0\ub9ac\uc998, \uc815\ucc45, \ud658\uacbd \ub4f1\uc774 \ubaa8\ub4c8\ud654 \ub418\uc5b4 \uc788\uc5b4 \uc720\uc5f0\ud558\uac8c \uc0ac\uc6a9 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/li>\n
- \uc131\ub2a5 \ucd5c\uc801\ud654: GPU \ubc0f TPU \uc9c0\uc6d0\uc73c\ub85c \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n
\uc0ac\uc6a9\ubc95<\/h3>\nimport ray\nfrom ray.rllib.agents import ppo\n\n# Ray \ucd08\uae30\ud654\nray.init()\n\n# \ud559\uc2b5 \ud658\uacbd \uad6c\uc131\nconfig = {\n \"env\": \"CartPole-v0\",\n}\n\n# PPO \uc5d0\uc774\uc804\ud2b8 \ud6c8\ub828\nagent = ppo.PPOTrainer(env=\"CartPole-v0\", config=config)\n\nfor i in range(100):\n result = agent.train()\n print(f\"Iteration: {i}, reward: {result['episode_reward_mean']}\")<\/code><\/pre>\n5. Keras-RL<\/h2>\n
Keras-RL\uc740 Keras\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \uac15\ud654\ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub2e4\uc591\ud55c RL \uc54c\uace0\ub9ac\uc998\uc744 \uac04\ud3b8\ud558\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\ub294 \ub3c4\uad6c\uc785\ub2c8\ub2e4.<\/p>\n
\ud2b9\uc9d5<\/h3>\n\n- Keras\uc640\uc758 \ud1b5\ud569: Keras\uc758 \uc0ac\uc6a9\ubc95\uacfc \uc720\uc0ac\ud558\uc5ec \ud559\uc2b5\uc774 \uc6a9\uc774\ud569\ub2c8\ub2e4.<\/li>\n
- \uc54c\uace0\ub9ac\uc998 \ub2e4\uc591\uc131: DQN, DDPG, A2C \ub4f1 \uc5ec\ub7ec \uc54c\uace0\ub9ac\uc998\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n
- \uc0ac\uc6a9\uc790 \uc815\uc758 \uac00\ub2a5: \uc720\uc5f0\ud55c \uad6c\uc870\ub97c \ud1b5\ud574 \uc0ac\uc6a9\uc790\uac00 \uc6d0\ud558\ub294 \ub300\ub85c \ubaa8\ub378\uc744 \uc815\uc758\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n
\uc0ac\uc6a9\ubc95<\/h3>\nfrom keras_rl.agents import DQNAgent\nfrom keras_rl.models import build_model\n\n# \ud658\uacbd \uc0dd\uc131\nenv = gym.make('CartPole-v0')\n\n# \ubaa8\ub378 \uad6c\ucd95\nmodel = build_model(env)\n\n# DQN \uc5d0\uc774\uc804\ud2b8 \uc0dd\uc131\nagent = DQNAgent(model=model, n_actions=env.action_space.n)\n\n# \ubaa8\ub378 \ud6c8\ub828\nagent.fit(env, n_episodes=100)<\/code><\/pre>\n6. ChainerRL<\/h2>\n
ChainerRL\uc740 Chainer \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \uac15\ud654\ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \uc0ac\uc6a9\uc790\ub4e4\uc774 \uc27d\uac8c \uc54c\uace0\ub9ac\uc998\uc744 \uc2e4\ud5d8\ud560 \uc218 \uc788\ub294 \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n
\ud2b9\uc9d5<\/h3>\n\n- \uc720\uc5f0\ud55c \ud504\ub85c\uadf8\ub798\ubc0d: Chainer\uc758 \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \ud65c\uc6a9\ud558\uba74 \ucf54\ub4dc\ub97c \uc27d\uac8c \uc218\uc815\ud558\uace0 \uc2e4\ud5d8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998: DQN, A3C, TRPO \ub4f1 \uc5ec\ub7ec \ucd5c\uc2e0 \uc54c\uace0\ub9ac\uc998 \uc81c\uacf5.<\/li>\n
- \uc0ac\uc6a9\uc790 \uce5c\ud654\uc801: \uc9c1\uad00\uc801\uc778 API\ub97c \ud1b5\ud574 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n
\uc0ac\uc6a9\ubc95<\/h3>\nimport chainer\nfrom chainer import Chain\nfrom chainer_rl.agents import DQN\nfrom chainer_rl.environments import gym\n\n# \ud658\uacbd \uc0dd\uc131\nenv = gym.make('CartPole-v0')\n\n# DQN \uc5d0\uc774\uc804\ud2b8 \uc0dd\uc131\nagent = DQN(env.observation_space.shape[0], env.action_space.n)\n\n# \ubaa8\ub378 \ud6c8\ub828\nfor episode in range(100):\n state = env.reset()\n done = False\n total_reward = 0\n \n while not done:\n action = agent.act(state)\n next_state, reward, done, info = env.step(action)\n agent.observe(reward, next_state, done)\n total_reward += reward\n print(f\"Episode: {episode}, Total Reward: {total_reward}\")<\/code><\/pre>\n\uacb0\ub860<\/h2>\n
\uac15\ud654\ud559\uc2b5\uc740 \ud765\ubbf8\ub86d\uace0 \ud601\uc2e0\uc801\uc778 \uae30\uc220\ub85c, \uc774\ub97c \uc704\ud55c \ub2e4\uc591\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \ub3c4\uad6c\ub4e4\uc774 \uc874\uc7ac\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 OpenAI Gym, TensorFlow Agents, Stable Baselines3, Ray RLLib, Keras-RL, ChainerRL\uc744 \uc18c\uac1c\ud558\uba70 \uac01 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 \ud2b9\uc9d5\uacfc \uc0ac\uc6a9\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc120\ud0dd\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \uac1c\uc778\uc758 \ud544\uc694\uc640 \uc120\ud638\uc5d0 \ub530\ub77c \ub2ec\ub77c\uc9c8 \uc218 \uc788\uc73c\uba70, \uac01 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \uc790\uc2e0\ub9cc\uc758 \uac15\ud654\ud559\uc2b5 \ubaa8\ub378\uc744 \uad6c\ud604\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
\uac15\ud654\ud559\uc2b5\uc758 \ubc1c\uc804\uc740 \uc55e\uc73c\ub85c\ub3c4 \uacc4\uc18d\ub420 \uac83\uc774\uba70, \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \uadf8 \uac00\ub2a5\uc131\uc744 \ubcf4\uc5ec\uc904 \uac83\uc785\ub2c8\ub2e4. \ubbf8\ub798\uc758 \uc5f0\uad6c\uc640 \ubc1c\uc804\uc744 \uae30\ub300\ud558\uba70, \uc774 \uae00\uc774 \uc5ec\ub7ec\ubd84\uc758 \uac15\ud654\ud559\uc2b5 \uc5ec\uc815\uc5d0 \ub3c4\uc6c0\uc774 \ub418\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"