{"id":40420,"date":"2024-11-04T01:51:36","date_gmt":"2024-11-04T01:51:36","guid":{"rendered":"https:\/\/atmokpo.com\/w\/?p=40420"},"modified":"2024-11-26T06:39:13","modified_gmt":"2024-11-26T06:39:13","slug":"6-%ea%b0%95%ed%99%94%ed%95%99%ec%8a%b5%ec%9d%98-%ec%97%ad%ec%82%ac%ec%99%80-%ec%a3%bc%ec%9a%94-%ec%9d%91%ec%9a%a9-%eb%b6%84%ec%95%bc-%ea%b2%8c%ec%9e%84-%eb%a1%9c%eb%b3%b4%ed%8b%b1%ec%8a%a4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/40420\/","title":{"rendered":"6. \uac15\ud654\ud559\uc2b5\uc758 \uc5ed\uc0ac\uc640 \uc8fc\uc694 \uc751\uc6a9 \ubd84\uc57c, \uac8c\uc784, \ub85c\ubcf4\ud2f1\uc2a4, \uae08\uc735 \ub4f1 \ub2e4\uc591\ud55c \uc751\uc6a9 \uc0ac\ub840"},"content":{"rendered":"<h2>6. \uac15\ud654\ud559\uc2b5\uc758 \uc5ed\uc0ac\uc640 \uc8fc\uc694 \uc751\uc6a9 \ubd84\uc57c<\/h2>\n<p>\uac15\ud654\ud559\uc2b5(Reinforcement Learning, RL)\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\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 \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uac15\ud654\ud559\uc2b5\uc740 \ubcf4\uc0c1\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud574 \ucd5c\uc801\uc758 \uc804\ub7b5\uc744 \ucc3e\ub294 \uacfc\uc815\uc73c\ub85c \uc815\uc758\ub418\uba70, \uc774\ub294 \uc778\uac04\uc758 \ud559\uc2b5 \ubc29\uc2dd\uacfc \uc720\uc0ac\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uac15\ud654\ud559\uc2b5\uc758 \uc5ed\uc0ac\uc640 \uadf8 \uc8fc\uc694 \uc751\uc6a9 \ubd84\uc57c\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>1. \uac15\ud654\ud559\uc2b5\uc758 \uc5ed\uc0ac<\/h3>\n<p>\uac15\ud654\ud559\uc2b5\uc758 \uae30\ucd08\ub294 1950\ub144\ub300\uc640 1960\ub144\ub300 \ucd08\ubc18\uc73c\ub85c \uac70\uc2ac\ub7ec \uc62c\ub77c\uac11\ub2c8\ub2e4. \ucd08\uae30\uc758 \uc5f0\uad6c\uc790\ub4e4\uc740 \uc2ec\ub9ac\ud559 \ubc0f \ud589\ub3d9\uacfc\ud559\uc5d0\uc11c \uc601\uac10\uc744 \ubc1b\uc544 \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba74\uc11c \ud559\uc2b5\ud558\ub294 \ubc29\uc2dd\uc744 \ubaa8\uc0c9\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li><strong>1950\ub144\ub300:<\/strong> \ucd08\uae30\uc758 \uac15\ud654\ud559\uc2b5 \uc774\ub860\uc740 \uc8fc\ub85c \uc2ec\ub9ac\ud559\uc758 \ud589\ub3d9\uc8fc\uc758 \uc774\ub860\uc5d0\uc11c \ucd9c\ubc1c\ud558\uc5ec \ubcf4\uc0c1\uacfc \ucc98\ubc8c\uc744 \ud1b5\ud574 \ud559\uc2b5\ud558\ub294 \uc6d0\ub9ac\ub97c \uae30\ubc18\uc73c\ub85c \ud588\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>1980\ub144\ub300:<\/strong> \uac15\ud654\ud559\uc2b5\uc758 \ubcf8\uaca9\uc801\uc778 \ubc1c\uc804\uc774 \uc774\ub8e8\uc5b4\uc84c\uc73c\uba70, \uc774 \uc2dc\uae30\uc5d0 Q-\ub7ec\ub2dd(Q-Learning)\uacfc \uac19\uc740 \uc54c\uace0\ub9ac\uc998\uc774 \uc81c\uc548\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc54c\uace0\ub9ac\uc998\uc740 \uc5d0\uc774\uc804\ud2b8\uac00 \ucd5c\uc801\uc758 \uc815\ucc45(Policy)\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uae30\uc5ec\ud588\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>1990\ub144\ub300:<\/strong> \uc774 \uc2dc\uae30\uc5d0 TD(Temporal Difference) \ud559\uc2b5 \ubc29\ubc95\uc774 \uc18c\uac1c\ub418\uc5c8\uace0, \uc774\ub294 \uac15\ud654\ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc758 \uc131\ub2a5\uc744 \ud55c\uce35 \ud5a5\uc0c1\uc2dc\ucf30\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc774 \uc2dc\uc791\ub418\uba74\uc11c \uac15\ud654\ud559\uc2b5\uacfc\uc758 \uc735\ud569\uc774 \uc774\ub8e8\uc5b4\uc9c0\uae30 \uc2dc\uc791\ud588\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>2010\ub144\ub300:<\/strong> \ub525\ub7ec\ub2dd\uc758 \ud601\uc2e0\uc774 \uac15\ud654\ud559\uc2b5\uc758 \ubc1c\uc804\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\ucce4\uc73c\uba70, \uc774\ub294 Deep Q-Network(DQN)\uc640 \uac19\uc740 \uc0c8\ub85c\uc6b4 \uc54c\uace0\ub9ac\uc998\uc758 \uac1c\ubc1c\ub85c \uc774\uc5b4\uc84c\uc2b5\ub2c8\ub2e4. DQN\uc740 Atari \uac8c\uc784\uc744 \ud1b5\ud574 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uba70 \uac15\ud654\ud559\uc2b5\uc758 \uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uc744 \uc81c\uc2dc\ud588\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>2020\ub144\ub300:<\/strong> \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \uac15\ud654\ud559\uc2b5\uc758 \uc751\uc6a9\uc740 \uae09\uc18d\ud788 \ud655\ub300\ub418\uace0 \uc788\uc73c\uba70, \ub2e4\uc591\ud55c \uc0b0\uc5c5 \ubd84\uc57c\uc5d0\uc11c \uc2e4\uc81c\ub85c \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc1c\uc804\uc740 \uc790\uc728 \uc8fc\ud589\ucc28, \ub85c\ubcf4\ud2f1\uc2a4, \uac8c\uc784 \uac1c\ubc1c \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \uc801\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>2. \uac15\ud654\ud559\uc2b5\uc758 \uc8fc\uc694 \uc751\uc6a9 \ubd84\uc57c<\/h3>\n<p>\uac15\ud654\ud559\uc2b5\uc740 \uadf8 \uc790\uccb4\ub85c\ub3c4 \uac15\ub825\ud55c \ud559\uc2b5 \ubc29\ubc95\uc774\uc9c0\ub9cc, \ud2b9\ud788 \ud2b9\uc815 \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \uadf8 \uc704\ub825\uc744 \ubc1c\ud718\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uac15\ud654\ud559\uc2b5\uc758 \uc8fc\uc694 \uc751\uc6a9 \ubd84\uc57c\uc785\ub2c8\ub2e4.<\/p>\n<h4>2.1 \uac8c\uc784<\/h4>\n<p>\uac15\ud654\ud559\uc2b5\uc758 \uac00\uc7a5 \uc720\uba85\ud55c \uc751\uc6a9 \ubd84\uc57c \uc911 \ud558\ub098\ub294 \uac8c\uc784\uc785\ub2c8\ub2e4. DQN\uc758 \ucd9c\ud604 \uc774\ud6c4, \uc54c\ud30c\uace0(AlphaGo)\uc640 \uac19\uc740 \uc2dc\uc2a4\ud15c\uc774 \uac1c\ubc1c\ub418\uc5b4 \ubc14\ub451\uacfc \uac19\uc740 \ubcf5\uc7a1\ud55c \uac8c\uc784\uc5d0\uc11c\ub3c4 \uc778\uac04 \ucd5c\uace0 \uc218\uc900\uc758 \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. \uac15\ud654\ud559\uc2b5\uc740 \uac8c\uc784\uc5d0\uc11c\uc758 \ub2e4\uc591\ud55c \uc804\ub7b5\uacfc \uc804\uc220\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uae30 \ub54c\ubb38\uc5d0 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n<pre><code>import numpy as np\nimport gym\n\nenv = gym.make('CartPole-v1')\nQ = np.zeros((env.observation_space.n, env.action_space.n))\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\nlr = 0.1\ndiscount_factor = 0.9\nnum_episodes = 5000\n\nfor episode in range(num_episodes):\n    state = env.reset()\n    done = False\n    while not done:\n        # \ud604\uc7ac \uc0c1\ud0dc\uc5d0\uc11c \uac00\uc7a5 \ud070 Q \uac12\uc758 \ud589\ub3d9 \uc120\ud0dd\n        if np.random.rand() &lt; epsilon:  # explore\n            action = env.action_space.sample()\n        else:  # exploit\n            action = np.argmax(Q[state, :])\n\n        # \ud658\uacbd\uc5d0\uc11c \ud589\ub3d9 \uc218\ud589\n        next_state, reward, done, _ = env.step(action)\n\n        # Q-\ub7ec\ub2dd \uc5c5\ub370\uc774\ud2b8\n        Q[state, action] = Q[state, action] + lr * (reward + discount_factor * np.max(Q[next_state, :]) - Q[state, action])\n\n        state = next_state<\/code><\/pre>\n<h4>2.2 \ub85c\ubcf4\ud2f1\uc2a4<\/h4>\n<p>\ub85c\ubcf4\ud2f1\uc2a4 \ubd84\uc57c\uc5d0\uc11c\ub3c4 \uac15\ud654\ud559\uc2b5\uc774 \uad11\ubc94\uc704\ud558\uac8c \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub85c\ubd07\uc740 \ud658\uacbd\uc744 \uc778\uc2dd\ud558\uace0 \uadf8\uc5d0 \ub530\ub77c \ud589\ub3d9\uc744 \uc870\uc815\ud574\uc57c \ud558\ub294 \ubcf5\uc7a1\ud55c \uc791\uc5c5\uc744 \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. \uac15\ud654\ud559\uc2b5\uc740 \uc774\ub7ec\ud55c \uc791\uc5c5\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \ub9e4\uc6b0 \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<p>\uc608\ub97c \ub4e4\uc5b4, \ub85c\ubd07\uc774 \ubb3c\uccb4\ub97c \uc9d1\uac70\ub098 \uc774\ub3d9\uc2dc\ud0a4\ub294 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \ub54c, \uac01 \ud589\ub3d9\uc5d0 \ub300\ud55c \ubcf4\uc0c1\uc744 \uc124\uc815\ud558\uc5ec \ub85c\ubd07\uc774 \ucd5c\uc801\uc758 \uacbd\ub85c\uc640 \ud589\ub3d9\uc744 \ud559\uc2b5\ud558\ub3c4\ub85d \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\uc2dd\uc740 \uc790\uc728\uc8fc\ud589\ucc28 \uac1c\ubc1c\uc5d0\ub3c4 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>import numpy as np\nimport tensorflow as tf\n\nenv = gym.make('FetchSlide-v1')\n\ndef build_model(input_shape, action_space):\n    model = tf.keras.Sequential()\n    model.add(tf.keras.layers.Dense(128, activation='relu', input_shape=input_shape))\n    model.add(tf.keras.layers.Dense(128, activation='relu'))\n    model.add(tf.keras.layers.Dense(action_space, activation='linear'))\n    return model\n\nmodel = build_model((env.observation_space.shape[0],), env.action_space.n)\n\n# \uc5d0\uc774\uc804\ud2b8\uc758 \ud559\uc2b5 \ub85c\uc9c1\ndef learn(state, action, reward, next_state):\n    target = reward + discount_factor * np.max(model.predict(next_state))\n    target_f = model.predict(state)\n    target_f[0][action] = target\n    model.fit(state, target_f, epochs=1, verbose=0)<\/code><\/pre>\n<h4>2.3 \uae08\uc735<\/h4>\n<p>\uae08\uc735 \ubd84\uc57c\uc5d0\uc11c\ub3c4 \uac15\ud654\ud559\uc2b5\uc740 \uac15\ub825\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\uc2dd \uac70\ub798, \uc790\uc0b0 \uad00\ub9ac \ub4f1 \ub2e4\uc591\ud55c \uae08\uc735 \uc758\uc0ac \uacb0\uc815\uc5d0\uc11c \uac15\ud654\ud559\uc2b5\uc744 \ud1b5\ud574 \ucd5c\uc801\uc758 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\uc608\ub97c \ub4e4\uc5b4, \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud560 \ub54c, \uacfc\uac70 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud2b9\uc815 \uc8fc\uc2dd\uc744 \ub9e4\uc218\ud558\uac70\ub098 \ub9e4\ub3c4\ud558\ub294 \ucd5c\uc801\uc758 \uc2dc\uc810\uc744 \uc120\ud0dd\ud558\ub294 \ub370 \uac15\ud654\ud559\uc2b5\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>import pandas as pd\nfrom sklearn.preprocessing import StandardScaler\n\ndata = pd.read_csv('stock_prices.csv')\nscaler = StandardScaler()\nscaled_data = scaler.fit_transform(data[['Open', 'High', 'Low', 'Close']])\n\n# \uac15\ud654\ud559\uc2b5 \uc5d0\uc774\uc804\ud2b8 \uc815\uc758\nclass TradingAgent:\n    def __init__(self):\n        self.state_size = scaled_data.shape[1]\n        self.action_size = 3  # Hold, Buy, Sell\n        self.memory = []\n\n    def act(self, state):\n        return np.random.choice(self.action_size)  # \ub79c\ub364 \uc120\ud0dd\n\nagent = TradingAgent()\n\n# \ud559\uc2b5 \ub85c\uc9c1\nfor e in range(num_episodes):\n    state = scaled_data[0]\n    for time in range(len(scaled_data)):\n        action = agent.act(state)\n        # \ubcf4\uc0c1 \uacc4\uc0b0 \ub4f1\uc744 \ucd94\uac00\n        # \ub2e4\uc74c \uc0c1\ud0dc \uc5c5\ub370\uc774\ud2b8\n<\/code><\/pre>\n<h3>\uacb0\ub860<\/h3>\n<p>\uac15\ud654\ud559\uc2b5\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ub9e4\uc6b0 \uc720\uc6a9\ud55c \uae30\uc220\ub85c \uc790\ub9ac \uc7a1\uace0 \uc788\uc73c\uba70, \uadf8 \ud6a8\uc728\uc131\uacfc \uac00\ub2a5\uc131\uc740 \uc55e\uc73c\ub85c\ub3c4 \uacc4\uc18d\ud574\uc11c \ubc1c\uc804\ud560 \uac83\uc785\ub2c8\ub2e4. \uac8c\uc784, \ub85c\ubcf4\ud2f1\uc2a4, \uae08\uc735 \ub4f1 \ub2e4\uc591\ud55c \uc0b0\uc5c5\uc5d0\uc11c \uc2e4\uc81c\ub85c \uc801\uc6a9\ub418\uace0 \uc788\ub294 \uc0ac\ub840\ub97c \ud1b5\ud574 \uac15\ud654\ud559\uc2b5\uc758 \uc7a0\uc7ac\ub825\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc1c\uc804\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ubbf8\ub798\ub97c \ubc1d\ud788\uace0 \uc788\uc73c\uba70, \uc6b0\ub9ac\ub294 \uc55e\uc73c\ub85c \ub354\uc6b1 \ud765\ubbf8\ub85c\uc6b4 \uc2dc\ub300\uc5d0 \uc811\uc5b4\ub4e4 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<p>\uc55e\uc73c\ub85c\ub3c4 \uac15\ud654\ud559\uc2b5\uc5d0 \ub300\ud55c \uc5f0\uad6c\uc640 \uac1c\ubc1c\uc774 \uacc4\uc18d\ub418\uc5b4, \uac01 \ubd84\uc57c\uc5d0\uc11c \ub354\uc6b1 \ud6a8\uacfc\uc801\uc778 \uc194\ub8e8\uc158\uc774 \ud0c4\uc0dd\ud558\uae30\ub97c \uae30\ub300\ud574\ubd05\ub2c8\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>6. \uac15\ud654\ud559\uc2b5\uc758 \uc5ed\uc0ac\uc640 \uc8fc\uc694 \uc751\uc6a9 \ubd84\uc57c \uac15\ud654\ud559\uc2b5(Reinforcement Learning, RL)\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\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 \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uac15\ud654\ud559\uc2b5\uc740 \ubcf4\uc0c1\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud574 \ucd5c\uc801\uc758 \uc804\ub7b5\uc744 \ucc3e\ub294 \uacfc\uc815\uc73c\ub85c \uc815\uc758\ub418\uba70, \uc774\ub294 \uc778\uac04\uc758 \ud559\uc2b5 \ubc29\uc2dd\uacfc \uc720\uc0ac\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uac15\ud654\ud559\uc2b5\uc758 \uc5ed\uc0ac\uc640 \uadf8 \uc8fc\uc694 \uc751\uc6a9 \ubd84\uc57c\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uac15\ud654\ud559\uc2b5\uc758 \uc5ed\uc0ac \uac15\ud654\ud559\uc2b5\uc758 \uae30\ucd08\ub294 1950\ub144\ub300\uc640 1960\ub144\ub300 \ucd08\ubc18\uc73c\ub85c &hellip; <a href=\"https:\/\/atmokpo.com\/w\/40420\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;6. \uac15\ud654\ud559\uc2b5\uc758 \uc5ed\uc0ac\uc640 \uc8fc\uc694 \uc751\uc6a9 \ubd84\uc57c, \uac8c\uc784, 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