{"id":29996,"date":"2024-10-28T03:19:03","date_gmt":"2024-10-28T03:19:03","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29996"},"modified":"2024-11-26T06:50:29","modified_gmt":"2024-11-26T06:50:29","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-%ea%b0%95%ed%99%94-%ed%95%99%ec%8a%b5%ec%9d%b4%eb%9e%80","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29996\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uac15\ud654 \ud559\uc2b5\uc774\ub780"},"content":{"rendered":"<p><body><\/p>\n<p>\uac15\ud654 \ud559\uc2b5(RL, Reinforcement Learning)\uc740 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \ubd84\uc57c \uc911 \ud558\ub098\ub85c, \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ud2b9\uc815 \uc0c1\ud0dc\uc5d0\uc11c \ud589\ub3d9\uc744 \uc120\ud0dd\ud558\uace0, \uadf8 \ud589\ub3d9\uc5d0 \ub300\ud55c \ubcf4\uc0c1\uc744 \ubc1b\uc544 \uc774\ub97c \ud1b5\ud574 \ud559\uc2b5\uc744 \ud569\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uac15\ud654 \ud559\uc2b5\uc758 \uae30\ubcf8 \uac1c\ub150, PyTorch\ub97c \uc774\uc6a9\ud55c \uad6c\ud604 \ubc29\ubc95, \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uac15\ud654 \ud559\uc2b5\uc758 \uc791\ub3d9 \ubc29\uc2dd\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. \uac15\ud654 \ud559\uc2b5\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n<p>\uac15\ud654 \ud559\uc2b5\uc758 \ud575\uc2ec \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc124\uba85\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\uc5d0\uc774\uc804\ud2b8(Agent)<\/strong>: \ud658\uacbd \ub0b4\uc5d0\uc11c \ud589\ub3d9\uc744 \ucde8\ud558\ub294 \uc8fc\uccb4\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\ud658\uacbd(Environment)<\/strong>: \uc5d0\uc774\uc804\ud2b8\uc758 \ud589\ub3d9\uc5d0 \ub530\ub77c \ubcc0\ud654\ud558\ub294 \uc2dc\uc2a4\ud15c\uc774\ub098 \uc138\uacc4\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\uc0c1\ud0dc(State)<\/strong>: \uc5d0\uc774\uc804\ud2b8\uac00 \ud604\uc7ac \ucc98\ud574 \uc788\ub294 \ud658\uacbd\uc758 \uc0c1\ud669\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/li>\n<li><strong>\ud589\ub3d9(Action)<\/strong>: \uc5d0\uc774\uc804\ud2b8\uac00 \uc120\ud0dd\ud560 \uc218 \uc788\ub294 \ub2e4\uc591\ud55c \ud589\ub3d9\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\ubcf4\uc0c1(Reward)<\/strong>: \uc5d0\uc774\uc804\ud2b8\uc758 \ud589\ub3d9\uc5d0 \ub300\ud574 \ud658\uacbd\uc774 \uc81c\uacf5\ud558\ub294 \ud53c\ub4dc\ubc31\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\uc815\ucc45(Policy)<\/strong>: \uc5d0\uc774\uc804\ud2b8\uac00 \uc8fc\uc5b4\uc9c4 \uc0c1\ud0dc\uc5d0\uc11c \uc5b4\ub5a4 \ud589\ub3d9\uc744 \uc120\ud0dd\ud560\uc9c0 \uacb0\uc815\ud558\ub294 \uc804\ub7b5\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\uac00\uce58 \ud568\uc218(Value Function)<\/strong>: \ud2b9\uc815 \uc0c1\ud0dc\uc5d0\uc11c\uc758 \uae30\ub300 \ubcf4\uc0c1\uc744 \ucd94\uc815\ud558\ub294 \ud568\uc218\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. \uac15\ud654 \ud559\uc2b5\uc758 \uacfc\uc815<\/h2>\n<p>\uac15\ud654 \ud559\uc2b5\uc758 \uae30\ubcf8 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ol>\n<li>\uc5d0\uc774\uc804\ud2b8\ub294 \ucd08\uae30 \uc0c1\ud0dc\ub97c \uad00\ucc30\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc815\ucc45\uc5d0 \ub530\ub77c \ud589\ub3d9\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/li>\n<li>\ud589\ub3d9\uc744 \ucde8\ud55c \ud6c4 \uc0c8\ub85c\uc6b4 \uc0c1\ud0dc\ub97c \uad00\ucc30\ud558\uace0 \ubcf4\uc0c1\uc744 \ubc1b\uc2b5\ub2c8\ub2e4.<\/li>\n<li>\ubcf4\uc0c1\uc744 \ubc14\ud0d5\uc73c\ub85c \uc815\ucc45\uc744 \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc774 \uacfc\uc815\uc744 \ubc18\ubcf5\ud558\uc5ec \ucd5c\uc801\uc758 \uc815\ucc45\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n<h2>3. \uac15\ud654 \ud559\uc2b5\uc758 \uc8fc\uc694 \uc54c\uace0\ub9ac\uc998<\/h2>\n<p>\uac15\ud654 \ud559\uc2b5\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 \uc8fc\uc694 \uc54c\uace0\ub9ac\uc998\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>Q-learning<\/strong>: \uac00\uce58 \uae30\ubc18 \ud559\uc2b5 \ubc29\ubc95\uc73c\ub85c, \uc5d0\uc774\uc804\ud2b8\ub294 Q-\uac12\uc744 \uc5c5\ub370\uc774\ud2b8\ud558\uc5ec \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc815\ucc45 \uacbd\ub7c9\ud654(Policy Gradient)<\/strong>: \uc815\ucc45\uc744 \uc9c1\uc811 \ud559\uc2b5\ud558\uba70, \ud655\ub960\uc801\uc778 \uc811\uadfc\ubc29\uc2dd\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>Actor-Critic<\/strong>: \uac00\uce58 \uae30\ubc18\uacfc \uc815\ucc45 \uae30\ubc18 \ubc29\ubc95\uc758 \uc870\ud569\uc73c\ub85c, \ub450 \uac00\uc9c0 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>4. PyTorch\ub97c \uc774\uc6a9\ud55c \uac15\ud654 \ud559\uc2b5 \uad6c\ud604<\/h2>\n<p>\uc774\ubc88 \uc139\uc158\uc5d0\uc11c\ub294 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uac15\ud654 \ud559\uc2b5 \uc608\uc81c\ub97c \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub294 OpenAI Gym\uc758 CartPole \ud658\uacbd\uc744 \uc0ac\uc6a9\ud558\uc5ec Q-learning \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n<h3>4.1. \ud658\uacbd \uc124\uc815<\/h3>\n<p>\uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0, CartPole \ud658\uacbd\uc744 \uc124\uc815\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>!pip install gym torch numpy\nimport gym\nimport numpy as np<\/code><\/pre>\n<h3>4.2. Q-learning \uc54c\uace0\ub9ac\uc998 \uad6c\ud604<\/h3>\n<p>\ub2e4\uc74c\uc73c\ub85c, Q-learning \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud569\ub2c8\ub2e4. Q \ud14c\uc774\ube14\uc744 \uc0dd\uc131\ud558\uace0, \u03b5-greedy \uc815\ucc45\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>class QLearningAgent:\n    def __init__(self, env):\n        self.env = env\n        self.q_table = np.zeros((env.observation_space.n, env.action_space.n))\n        self.learning_rate = 0.1\n        self.discount_factor = 0.95\n        self.epsilon = 0.1\n\n    def choose_action(self, state):\n        if np.random.rand() &lt; self.epsilon:\n            return self.env.action_space.sample()\n        else:\n            return np.argmax(self.q_table[state])\n\n    def learn(self, state, action, reward, next_state):\n        best_next_action = np.argmax(self.q_table[next_state])\n        td_target = reward + self.discount_factor * self.q_table[next_state][best_next_action]\n        td_delta = td_target - self.q_table[state][action]\n        self.q_table[state][action] += self.learning_rate * td_delta<\/code><\/pre>\n<h3>4.3. \ud559\uc2b5 \uacfc\uc815<\/h3>\n<p>\uc774\uc81c \uc5d0\uc774\uc804\ud2b8\ub97c \ud559\uc2b5\uc2dc\ud0a4\ub294 \uba54\uc778 \ub8e8\ud504\ub97c \uc791\uc131\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>env = gym.make('CartPole-v1')\nagent = QLearningAgent(env)\n\nepisodes = 1000\nfor episode in range(episodes):\n    state = env.reset()\n    done = False\n    while not done:\n        action = agent.choose_action(state)\n        next_state, reward, done, _ = env.step(action)\n        agent.learn(state, action, reward, next_state)\n        state = next_state<\/code><\/pre>\n<h3>4.4. \ud559\uc2b5 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n<p>\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ud6c4, \uc5d0\uc774\uc804\ud2b8\uc758 \ud589\ub3d9\uc744 \uc2dc\uac01\ud654\ud558\uc5ec \uacb0\uacfc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>total_reward = 0\nstate = env.reset()\ndone = False\nwhile not done:\n    action = np.argmax(agent.q_table[state])\n    state, reward, done, _ = env.step(action)\n    total_reward += reward\n    env.render()\n\nprint(f'Total Reward: {total_reward}')\nenv.close()<\/code><\/pre>\n<h2>5. \uacb0\ub860<\/h2>\n<p>\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \uac15\ud654 \ud559\uc2b5\uc758 \uae30\ubcf8 \uac1c\ub150\uc744 \uc124\uba85\ud558\uace0, PyTorch\uc640 OpenAI Gym\uc744 \uc774\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c Q-learning \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud574 \ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uac15\ud654 \ud559\uc2b5\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \uc801\uc6a9\ub420 \uc218 \uc788\ub294 \uac15\ub825\ud55c \uae30\uc220\uc774\uba70, \uc55e\uc73c\ub85c\ub3c4 \ub9ce\uc740 \ubc1c\uc804\uc774 \uae30\ub300\ub429\ub2c8\ub2e4. \ub2e4\uc74c \uae00\uc5d0\uc11c\ub294 \ub354\uc6b1 \uc2ec\ud654\ub41c \uc8fc\uc81c\ub97c \ub2e4\ub8e8\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>6. \ucc38\uace0 \uc790\ub8cc<\/h2>\n<ul>\n<li>OpenAI Gym: <a href=\"https:\/\/gym.openai.com\/\">https:\/\/gym.openai.com\/<\/a><\/li>\n<li>PyTorch: <a href=\"https:\/\/pytorch.org\/\">https:\/\/pytorch.org\/<\/a><\/li>\n<li>Reinforcement Learning: An Introduction (2nd Edition) by Richard S. Sutton and Andrew G. Barto<\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uac15\ud654 \ud559\uc2b5(RL, Reinforcement Learning)\uc740 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \ubd84\uc57c \uc911 \ud558\ub098\ub85c, \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ud2b9\uc815 \uc0c1\ud0dc\uc5d0\uc11c \ud589\ub3d9\uc744 \uc120\ud0dd\ud558\uace0, \uadf8 \ud589\ub3d9\uc5d0 \ub300\ud55c \ubcf4\uc0c1\uc744 \ubc1b\uc544 \uc774\ub97c \ud1b5\ud574 \ud559\uc2b5\uc744 \ud569\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uac15\ud654 \ud559\uc2b5\uc758 \uae30\ubcf8 \uac1c\ub150, PyTorch\ub97c \uc774\uc6a9\ud55c \uad6c\ud604 \ubc29\ubc95, \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uac15\ud654 \ud559\uc2b5\uc758 \uc791\ub3d9 \ubc29\uc2dd\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uac15\ud654 \ud559\uc2b5\uc758 \uae30\ubcf8 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29996\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uac15\ud654 \ud559\uc2b5\uc774\ub780&#8221;<\/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":[33],"tags":[],"class_list":["post-29996","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uac15\ud654 \ud559\uc2b5\uc774\ub780 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/29996\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uac15\ud654 \ud559\uc2b5\uc774\ub780 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uac15\ud654 \ud559\uc2b5(RL, Reinforcement Learning)\uc740 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \ubd84\uc57c \uc911 \ud558\ub098\ub85c, \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4. \uc5d0\uc774\uc804\ud2b8\ub294 \ud2b9\uc815 \uc0c1\ud0dc\uc5d0\uc11c \ud589\ub3d9\uc744 \uc120\ud0dd\ud558\uace0, \uadf8 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