\ud53c\ucc98 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1<\/li>\n<\/ul>\n3. \uc54c\uace0\ub9ac\uc998 \uc120\ud0dd \ubc0f \uad6c\ud604<\/h2>\n
\uac15\ud654\ud559\uc2b5\uc5d0\ub294 \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc774 \uc874\uc7ac\ud569\ub2c8\ub2e4. \ud504\ub85c\uc81d\ud2b8\uc758 \ud2b9\uc131\uc5d0 \ub9de\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c\ub294 Q-learning, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO) \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \uc54c\uace0\ub9ac\uc998 \uc774\ud574<\/h3>\n
\uac01 \uc54c\uace0\ub9ac\uc998\uc758 \ud2b9\uc9d5\uacfc \uc7a5\ub2e8\uc810\uc744 \uc774\ud574\ud558\uace0, \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc801\ud569\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, DQN\uc740 \uc774\ubbf8\uc9c0\uc640 \uac19\uc740 \uace0\ucc28\uc6d0 \uc785\ub825 \ub370\uc774\ud130\ub97c \uc798 \ucc98\ub9ac\ud560 \uc218 \uc788\uc9c0\ub9cc, \ud559\uc2b5 \uc18d\ub3c4\uac00 \ub290\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.2 \uc54c\uace0\ub9ac\uc998 \uad6c\ud604<\/h3>\n
\uc120\ud0dd\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\uace0, \ub2e4\uc591\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc124\uc815\ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \uc608\uc81c\ub294 DQN\uc744 \uad6c\ud604\ud558\ub294 \uac04\ub2e8\ud55c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
import gym\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import layers\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\nnum_episodes = 500\nmax_steps = 200\nlearning_rate = 0.001\n\n# \ud658\uacbd \ucd08\uae30\ud654\nenv = gym.make('CartPole-v1')\n\n# Q \ub124\ud2b8\uc6cc\ud06c \uad6c\ucd95\nmodel = tf.keras.Sequential([\n layers.Dense(24, activation='relu', input_shape=(env.observation_space.shape[0],)),\n layers.Dense(24, activation='relu'),\n layers.Dense(env.action_space.n, activation='linear')\n])\n\noptimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\nloss_function = tf.keras.losses.Huber()\n\n# DQN \ud559\uc2b5 \ud568\uc218\ndef train_dqn():\n for episode in range(num_episodes):\n state = env.reset()\n for step in range(max_steps):\n action = select_action(state)\n next_state, reward, done, _ = env.step(action)\n store_transition(state, action, reward, next_state, done)\n state = next_state\n \n if done:\n break\n\ndef select_action(state):\n if np.random.rand() < epsilon:\n return env.action_space.sample() # \ub79c\ub364 \ud589\ub3d9\n q_values = model.predict(state[np.newaxis])\n return np.argmax(q_values[0]) # \ucd5c\ub300 Q\uac12\uc5d0 \ud574\ub2f9\ud558\ub294 \ud589\ub3d9 \uc120\ud0dd\n <\/code><\/pre>\n4. \ud559\uc2b5 \ubc0f \ud3c9\uac00<\/h2>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0 \uacb0\uacfc\ub97c \ud3c9\uac00\ud569\ub2c8\ub2e4. \ud559\uc2b5 \uacfc\uc815\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud558\uc5ec \ubaa8\ub378\uc774 \uc798 \ud559\uc2b5\ub418\uace0 \uc788\ub294\uc9c0 \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4. \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c\uc758 \uc131\ub2a5 \uc9c0\ud45c\ub97c \ud1b5\ud574 \uc870\uae30\uc5d0 \ud559\uc2b5\uc744 \uc911\ub2e8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c \ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ud6c4, \ubcc4\ub3c4\uc758 \ud14c\uc2a4\ud2b8 \ud658\uacbd\uc5d0\uc11c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n
5. \uacb0\uacfc \ubd84\uc11d<\/h2>\n
\ubaa8\ub378\uc774 \ud559\uc2b5\ub41c \ud6c4, \uadf8 \uacb0\uacfc\ub97c \ubd84\uc11d\ud558\uace0 \uc2dc\uac01\ud654\ud558\ub294 \uac83\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ud559\uc2b5\ud55c \uc815\ucc45\uc774 \uc2e4\uc81c\ub85c \uc5b4\ub5bb\uac8c \uc791\ub3d9\ud558\ub294\uc9c0 \ud3c9\uac00\ud558\uace0, \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\ub294 \ubc29\ud5a5\uc744 \ucc3e\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud504\ub85c\uc81d\ud2b8\uc758 \uc131\uacf5 \uc5ec\ubd80\ub97c \ud310\ub2e8\ud558\ub294 \uc911\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4.<\/p>\n
6. \ubb38\uc11c\ud654 \ubc0f \uacf5\uc720<\/h2>\n
\ud504\ub85c\uc81d\ud2b8\uc758 \uacb0\uacfc\ub97c \ubb38\uc11c\ud654\ud558\uace0, \uacf5\uc720\ud568\uc73c\ub85c\uc368 \ub2e4\ub978 \uac1c\ubc1c\uc790\ub4e4\uacfc\uc758 \ud53c\ub4dc\ubc31\uc744 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. GitHub\uc640 \uac19\uc740 \ud50c\ub7ab\ud3fc\uc744 \ud1b5\ud574 \ucf54\ub4dc\ub97c \uacf5\uc720\ud558\uace0, \uc0ac\uc6a9\ud55c \ud658\uacbd \ubc0f \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \uc815\ubcf4\ub97c \uc81c\uacf5\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
7. \uc9c0\uc18d\uc801\uc778 \uac1c\uc120<\/h2>\n
\ud504\ub85c\uc81d\ud2b8\uac00 \uc644\ub8cc\ub41c \ud6c4\uc5d0\ub3c4 \uc9c0\uc18d\uc801\uc73c\ub85c \ubaa8\ub378\uc744 \uac1c\uc120\ud560 \uc218 \uc788\ub294 \uae30\ud68c\ub97c \ucc3e\uc2b5\ub2c8\ub2e4. \ucd94\uac00 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uac70\ub098, \uc54c\uace0\ub9ac\uc998\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud558\uc5ec \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uacb0\ub860<\/h2>\n
\uac15\ud654\ud559\uc2b5 \ud504\ub85c\uc81d\ud2b8\ub294 \ub2e4\uc591\ud55c \uae30\uc220\uc801 \ub3c4\uc804\uacfc\uc81c\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc73c\uba70, \uc131\uacf5\uc801\uc778 \uacb0\uacfc\ub97c \uc5bb\uae30 \uc704\ud574\uc11c\ub294 \uccb4\uacc4\uc801\uc778 \uacc4\ud68d \ubc0f \uad00\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \ubaa9\ud45c\ub97c \uba85\ud655\ud788 \ud558\uace0, \uc801\uc808\ud55c \ub370\uc774\ud130 \uc218\uc9d1\uacfc \uc54c\uace0\ub9ac\uc998 \uc120\ud0dd, \uc131\uacfc \ud3c9\uac00 \ubc0f \uac1c\uc120 \ud504\ub85c\uc138\uc2a4\ub97c \ud1b5\ud574 \ud504\ub85c\uc81d\ud2b8\uc758 \uc131\uacf5 \uac00\ub2a5\uc131\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ubcf8 \uae00\uc774 \uc5ec\ub7ec\ubd84\uc758 \uac15\ud654\ud559\uc2b5 \ud504\ub85c\uc81d\ud2b8\uc5d0 \ub3c4\uc6c0\uc774 \ub418\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \ub354\uc6b1 \ubc1c\uc804\ub41c \uae30\uc220\uacfc \ubc29\ubc95\ub860\uc774\ub860\uc744 \uc11c\ub85c \uacf5\uc720\ud558\uba70, \ud568\uaed8 \uc131\uc7a5\ud574 \ub098\uac00\uae38 \uae30\ub300\ud569\ub2c8\ub2e4.<\/p>\n