{"id":40412,"date":"2024-11-04T01:51:32","date_gmt":"2024-11-04T01:51:32","guid":{"rendered":"https:\/\/atmokpo.com\/w\/?p=40412"},"modified":"2024-11-26T06:39:14","modified_gmt":"2024-11-26T06:39:14","slug":"48-rl-%eb%8c%80%ec%8b%a0-%ec%82%ac%ec%9a%a9%ed%95%a0-%ec%88%98-%ec%9e%88%eb%8a%94-%eb%8c%80%ec%95%88-%ec%a0%91%ea%b7%bc%eb%b2%95-%ec%86%8c%ea%b0%9c-%ec%83%81%ed%99%a9%ec%97%90-%eb%94%b0%eb%a5%b8","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/40412\/","title":{"rendered":"48. RL \ub300\uc2e0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub300\uc548 \uc811\uadfc\ubc95 \uc18c\uac1c, \uc0c1\ud669\uc5d0 \ub530\ub978 \ucd5c\uc801\uc758 \uc120\ud0dd \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\uac15\ud654\ud559\uc2b5(RL)\uc740 \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc640 \ud658\uacbd\uc5d0 \ub300\ud55c \uc0c1\ud638 \uc791\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \uc5d0\uc774\uc804\ud2b8\ub97c \ud559\uc2b5\uc2dc\ud0a4\ub294 \uac15\ub825\ud55c \ubc29\ubc95\uc774\uc9c0\ub9cc, \ud56d\uc0c1 \ucd5c\uc120\uc758 \uc120\ud0dd\uc774 \uc544\ub2d0 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 RL\uc758 \ub300\uc548\uc73c\ub85c \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uc5ec\ub7ec \uc811\uadfc\ubc95\uc744 \uc18c\uac1c\ud558\uace0, \uac01 \ubc29\ubc95\uc774 \uc801\uc808\ud55c \uc0c1\ud669\uc5d0\uc11c \uc5b4\ub5bb\uac8c \ud6a8\uacfc\uc801\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc804\ud1b5\uc801\uc778 \uae30\uacc4 \ud559\uc2b5 \uc811\uadfc\ubc95<\/h2>\n

\uae30\uacc4 \ud559\uc2b5\uc740 \uac15\ud654\ud559\uc2b5\uc758 \ud55c \ubd84\uc57c\ub85c, \uc8fc\ub85c \uc9c0\ub3c4 \ud559\uc2b5\uacfc \ube44\uc9c0\ub3c4 \ud559\uc2b5\uc73c\ub85c \uad6c\ubd84\ub429\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc811\uadfc\ubc95\ub4e4\uc740 \ub370\uc774\ud130\uac00 \ucda9\ubd84\ud788 \uc81c\uacf5\ub420 \ub54c \uac15\ub825\ud55c \uacb0\uacfc\ub97c \ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

1.1 \uc9c0\ub3c4 \ud559\uc2b5<\/h3>\n

\uc9c0\ub3c4 \ud559\uc2b5\uc740 \uc785\ub825\uacfc \ucd9c\ub825\uc774 \uba85\ud655\ud788 \uc815\uc758\ub41c \ub370\uc774\ud130\uc14b\uc744 \ubc14\ud0d5\uc73c\ub85c \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc8fc\ud0dd \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uae30 \uc704\ud574 \uacfc\uac70\uc758 \uac70\ub798 \ub370\uc774\ud130\uc640 \ud574\ub2f9 \uac00\uaca9\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n

\uc608\uc81c: \uc8fc\ud0dd \uac00\uaca9 \uc608\uce21<\/h4>\n
import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('housing_data.csv')\nX = data[['num_rooms', 'num_bathrooms', 'land_size']]\ny = data['price']\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \ubaa8\ub378 \ud6c8\ub828\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n# \uc608\uce21\npredictions = model.predict(X_test)\nprint(predictions)\n<\/code><\/pre>\n
1.2 \ube44\uc9c0\ub3c4 \ud559\uc2b5<\/h3>\n
\ube44\uc9c0\ub3c4 \ud559\uc2b5\uc740 \ub808\uc774\ube14\uc774 \uc5c6\ub294 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uc5ec \ud328\ud134\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ud074\ub7ec\uc2a4\ud130\ub9c1, \ucc28\uc6d0 \ucd95\uc18c \ub4f1\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c: K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1<\/h4>\n
from sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\n\n# \ub370\uc774\ud130 \uc0dd\uc131\ndata = [[1, 2], [1, 4], [1, 0],\n        [4, 2], [4, 4], [4, 0]]\n\n# KMeans \ubaa8\ub378\nkmeans = KMeans(n_clusters=2, random_state=0).fit(data)\n\n# \uc608\uce21 \ubc0f \uc2dc\uac01\ud654\nlabels = kmeans.labels_\nplt.scatter([d[0] for d in data], [d[1] for d in data], c=labels)\nplt.show()\n<\/code><\/pre>\n
2. \uc9c4\ud654 \uc54c\uace0\ub9ac\uc998<\/h2>\n
\uc9c4\ud654 \uc54c\uace0\ub9ac\uc998\uc740 \uc790\uc5f0 \uc120\ud0dd\uc758 \uc6d0\ub9ac\ub97c \uae30\ubc18\uc73c\ub85c \ud558\ub294 \ucd5c\uc801\ud654 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc720\uc804\uc790 \uc54c\uace0\ub9ac\uc998(GA)\uc740 \uac00\uc7a5 \ub110\ub9ac \uc54c\ub824\uc9c4 \uc9c4\ud654 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c, \ubcf5\uc7a1\ud55c \ubb38\uc81c\uc5d0 \ub300\ud55c \ud574\ub97c \ud0d0\uc0c9\ud558\ub294 \ub370 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n
2.1 \uc720\uc804\uc790 \uc54c\uace0\ub9ac\uc998<\/h3>\n
\uc720\uc804\uc790 \uc54c\uace0\ub9ac\uc998\uc740 \ud574\ub97c \uc720\uc804\uc790\uc640 \uac19\uc740 \uad6c\uc870\ub85c \ubcc0\ud658\ud558\uc5ec, \uc138\ub300\ubcc4\ub85c \uad50\ubc30, \ub3cc\uc5f0\ubcc0\uc774\ub97c \ud1b5\ud574 \ucd5c\uc801 \ud574\ub97c \ucc3e\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c: \uc720\uc804\uc790 \uc54c\uace0\ub9ac\uc998\uc744 \uc774\uc6a9\ud55c \ucd5c\uc801\ud654<\/h4>\n
import numpy as np\n\ndef fitness_function(x):\n    return -x**2 + 10\n\n# \ucd08\uae30 \uac1c\uccb4\uad70 \uc0dd\uc131\npopulation = np.random.rand(10) * 10\n\nfor generation in range(100):\n    # \uc801\ud569\ub3c4 \ud3c9\uac00\n    fitness = fitness_function(population)\n    \n    # \uc120\ud0dd\n    selected = population[np.argsort(-fitness)[:5]]\n    \n    # \uad50\ubc30 \ubc0f \ub3cc\uc5f0\ubcc0\uc774\n    offspring = []\n    for i in range(5):\n        parent1 = selected[np.random.randint(5)]\n        parent2 = selected[np.random.randint(5)]\n        child = (parent1 + parent2) \/ 2 + np.random.randn() * 0.5\n        offspring.append(child)\n    \n    population = np.concatenate([selected, offspring])\n\nbest_solution = population[np.argmax(fitness_function(population))]\nprint(f\"\ucd5c\uc0c1\uc758 \uc194\ub8e8\uc158: {best_solution}\")\n<\/code><\/pre>\n
3. \ucd5c\uc801 \uc81c\uc5b4 \uc774\ub860<\/h2>\n
\ucd5c\uc801 \uc81c\uc5b4 \uc774\ub860\uc740 \ud2b9\uc815 \uc2dc\uc2a4\ud15c\uc758 \ub3d9\uc801 \ud589\ub3d9\uc744 \ucd5c\uc801\ud654\ud558\uae30 \uc704\ud574 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774\ub294 RL\uacfc \uc720\uc0ac\ud558\uc9c0\ub9cc, \uba85\ud655\ud55c \uc218\ud559\uc801 \ubaa8\ub378\uc774 \uc788\uc744 \ub54c \uc8fc\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
3.1 PID \uc81c\uc5b4\uae30<\/h3>\n
PID \uc81c\uc5b4\uae30\ub294 \ube44\ub840-\uc801\ubd84-\ubbf8\ubd84 \uc81c\uc5b4\uae30\ub97c \uc758\ubbf8\ud558\uba70, \uc2dc\uc2a4\ud15c\uc758 \ucd9c\ub825\uacfc \uc6d0\ud558\ub294 \ubaa9\ud45c\uce58 \uac04\uc758 \ucc28\uc774\ub97c \uc904\uc774\uae30 \uc704\ud574 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c: \uac04\ub2e8\ud55c PID \uc81c\uc5b4\uae30 \uad6c\ud604<\/h4>\n
class PIDController:\n    def __init__(self, p_gain, i_gain, d_gain):\n        self.p_gain = p_gain\n        self.i_gain = i_gain\n        self.d_gain = d_gain\n        self.prev_error = 0\n        self.integral = 0\n\n    def update(self, setpoint, actual):\n        error = setpoint - actual\n        self.integral += error\n        derivative = error - self.prev_error\n        \n        output = (self.p_gain * error + \n                  self.i_gain * self.integral + \n                  self.d_gain * derivative)\n\n        self.prev_error = error\n        return output\n\npid = PIDController(1.0, 0.1, 0.05)\noutput = pid.update(100, 90)\nprint(f\"\uc81c\uc5b4 \ucd9c\ub825: {output}\")\n<\/code><\/pre>\n
4. \uba85\uc2dc\uc801 \ubaa8\ub378\ub9c1<\/h2>\n
\uba85\uc2dc\uc801 \ubaa8\ub378\ub9c1\uc740 \uc2dc\uc2a4\ud15c\uc758 \ub3d9\uc791\uc744 \uc218\ud559\uc801\uc73c\ub85c \ubaa8\ub378\ub9c1\ud558\uc5ec \ucd5c\uc801 \ud574\ub97c \ucc3e\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub9c8\ub974\ucf54\ud504 \uacb0\uc815 \uacfc\uc815(MDP)\uc740 \ub9e4\uc6b0 \uc720\uba85\ud55c \ubaa8\ub378\uc785\ub2c8\ub2e4.<\/p>\n
4.1 \ub9c8\ub974\ucf54\ud504 \uacb0\uc815 \uacfc\uc815<\/h3>\n
MDP\ub294 \uc0c1\ud0dc, \ud589\ub3d9, \ubcf4\uc0c1\uc73c\ub85c \uad6c\uc131\ub418\uba70, \uac15\ud654\ud559\uc2b5\uc758 \uae30\ucd08\uac00 \ub429\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uc815\ucc45, \uac00\uce58 \ud568\uc218 \ub4f1\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \uacb0\uc815\ud569\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c: MDP \uad6c\ud604<\/h4>\n
class MDP:\n    def __init__(self, states, actions, transition_probabilities, rewards):\n        self.states = states\n        self.actions = actions\n        self.transition_probabilities = transition_probabilities\n        self.rewards = rewards\n\n    def value_iteration(self, discount_factor=1.0):\n        value = {s: 0 for s in self.states}\n        \n        while True:\n            next_value = value.copy()\n            for s in self.states:\n                next_value[s] = max(\n                    sum(self.transition_probabilities[s, a, s_next] * \n                        (self.rewards[s, a, s_next] + discount_factor * value[s_next]) \n                        for s_next in self.states) for a in self.actions)\n            if all(abs(next_value[s] - value[s]) < 1e-6 for s in self.states):\n                return next_value\n            value = next_value\n\nstates = ['A', 'B']\nactions = ['move']\ntransition_probabilities = {('A', 'move', 'B'): 1.0, ('B', 'move', 'A'): 1.0}\nrewards = {('A', 'move', 'B'): 10, ('B', 'move', 'A'): 5}\n\nmdp = MDP(states, actions, transition_probabilities, rewards)\noptimal_value = mdp.value_iteration()\nprint(f\"\ucd5c\uc801 \uac00\uce58: {optimal_value}\")\n<\/code><\/pre>\n
5. \uc0c1\ud669\uc5d0 \ub530\ub978 \ucd5c\uc801\uc758 \uc120\ud0dd \ubc29\ubc95<\/h2>\n
\ub300\uc548 \uc811\uadfc\ubc95\ub4e4\uc740 \uac01\uae30 \ub2e4\ub978 \uc0c1\ud669\uc5d0\uc11c \uc7a5\uc810\uacfc \ub2e8\uc810\uc774 \uc788\uae30 \ub54c\ubb38\uc5d0 \ud2b9\uc815 \ubb38\uc81c\uc5d0 \uac00\uc7a5 \uc801\ud569\ud55c \ubc29\ubc95\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
5.1 \ubb38\uc81c\uc758 \ubcf5\uc7a1\uc131<\/h3>\n
\ubb38\uc81c\uac00 \uac04\ub2e8\ud558\uace0 \ub370\uc774\ud130\uac00 \ucd95\uc801\ub41c \uacbd\uc6b0\uc5d0\ub294 \uc9c0\ub3c4 \ud559\uc2b5\uc774 \uc720\ub9ac\ud558\uc9c0\ub9cc, \ubcf5\uc7a1\ud55c \ub3d9\uc801 \ud658\uacbd\uc5d0\uc11c\ub294 \uac15\ud654\ud559\uc2b5\uc774\ub098 \uc9c4\ud654 \uc54c\uace0\ub9ac\uc998\uc774 \uc801\ud569\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.2 \ub370\uc774\ud130\uc758 \uac00\uc6a9\uc131<\/h3>\n
\uac15\ud654\ud559\uc2b5\uc740 \ub370\uc774\ud130\uac00 \ubd80\uc871\ud560 \ub54c \ud6a8\uacfc\uc801\uc77c \uc218 \uc788\uc9c0\ub9cc, \ucda9\ubd84\ud55c \ub370\uc774\ud130\uac00 \uc788\ub294 \uacbd\uc6b0\uc5d0\ub294 \uc804\ud1b5\uc801\uc778 \uae30\uacc4 \ud559\uc2b5\uc774 \ub354 \ube60\ub974\uace0 \uc815\ud655\ud55c \uacb0\uacfc\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n
5.3 \uc2dc\uc2a4\ud15c\uc758 \ud589\ub3d9 \uc608\uce21 \uac00\ub2a5\uc131<\/h3>\n
MDP\uc640 \uac19\uc740 \uba85\uc2dc\uc801 \ubaa8\ub378\ub9c1 \ubc29\ubc95\uc740 \uc2dc\uc2a4\ud15c\uc758 \ud589\ub3d9\uc744 \uc815\ubc00\ud558\uac8c \uc608\uce21\ud560 \uc218 \uc788\ub294 \uacbd\uc6b0 \uc720\ub9ac\ud558\uba70, \uc2dc\uc2a4\ud15c\uc758 \ubaa8\ub378\uc774 \ubd88\ud655\uc2e4\ud560 \uacbd\uc6b0 \uac15\ud654\ud559\uc2b5\uc774 \ub354 \uc801\ud569\ud569\ub2c8\ub2e4.<\/p>\n
\uacb0\ub860<\/h2>\n
\uac15\ud654\ud559\uc2b5\uc740 \uac15\ub825\ud55c \ub3c4\uad6c\uc9c0\ub9cc, \ud56d\uc0c1 \uc0c1\ud669\uc5d0 \ub9de\ub294 \ucd5c\uc801\uc758 \uc120\ud0dd\uc774 \uc544\ub2d9\ub2c8\ub2e4. \uae30\uacc4 \ud559\uc2b5, \uc9c4\ud654 \uc54c\uace0\ub9ac\uc998, \ucd5c\uc801 \uc81c\uc5b4 \uc774\ub860 \ub4f1 \uc5ec\ub7ec \ub300\uc548 \uc811\uadfc\ubc95\ub4e4\uc774 \uc788\uc73c\uba70, \uac01 \uc811\uadfc\ubc95\uc740 \ud2b9\uc815 \ubb38\uc81c\uc5d0 \ub9de\ucdb0 \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4. \ubb38\uc81c\uc758 \ud2b9\uc131\uc744 \uace0\ub824\ud558\uace0 \ub2e4\uc591\ud55c \uc811\uadfc\ubc95\uc744 \uc544\ub294 \uac83\uc740 \uc131\uacf5\uc801\uc778 AI \ubaa8\ub378 \uac1c\ubc1c\uc5d0 \ud070 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4. \uc774\uc81c \uc5ec\ub7ec\ubd84\uc740 \uac01 \uc811\uadfc\ubc95\uc758 \uc7a5\uc810\uacfc \ub2e8\uc810\uc744 \uc774\ud574\ud558\uace0, \uc0c1\ud669\uc5d0 \ub9de\ub294 \ucd5c\uc801\uc758 \ubc29\ubc95\uc744 \uc120\ud0dd\ud558\ub294 \ub370 \ud544\uc694\ud55c \uc815\ubcf4\ub97c \uac00\uc84c\uc2b5\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uac15\ud654\ud559\uc2b5(RL)\uc740 \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc640 \ud658\uacbd\uc5d0 \ub300\ud55c \uc0c1\ud638 \uc791\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \uc5d0\uc774\uc804\ud2b8\ub97c \ud559\uc2b5\uc2dc\ud0a4\ub294 \uac15\ub825\ud55c \ubc29\ubc95\uc774\uc9c0\ub9cc, \ud56d\uc0c1 \ucd5c\uc120\uc758 \uc120\ud0dd\uc774 \uc544\ub2d0 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 RL\uc758 \ub300\uc548\uc73c\ub85c \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uc5ec\ub7ec \uc811\uadfc\ubc95\uc744 \uc18c\uac1c\ud558\uace0, \uac01 \ubc29\ubc95\uc774 \uc801\uc808\ud55c \uc0c1\ud669\uc5d0\uc11c \uc5b4\ub5bb\uac8c \ud6a8\uacfc\uc801\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc804\ud1b5\uc801\uc778 \uae30\uacc4 \ud559\uc2b5 \uc811\uadfc\ubc95 \uae30\uacc4 \ud559\uc2b5\uc740 \uac15\ud654\ud559\uc2b5\uc758 \ud55c \ubd84\uc57c\ub85c, \uc8fc\ub85c \uc9c0\ub3c4 \ud559\uc2b5\uacfc \ube44\uc9c0\ub3c4 \ud559\uc2b5\uc73c\ub85c \uad6c\ubd84\ub429\ub2c8\ub2e4. … \ub354 \ubcf4\uae30 “48. RL \ub300\uc2e0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub300\uc548 \uc811\uadfc\ubc95 \uc18c\uac1c, \uc0c1\ud669\uc5d0 \ub530\ub978 \ucd5c\uc801\uc758 \uc120\ud0dd \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-40412","post","type-post","status-publish","format-standard","hentry","category-213"],"yoast_head":"\n48. RL \ub300\uc2e0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub300\uc548 \uc811\uadfc\ubc95 \uc18c\uac1c, \uc0c1\ud669\uc5d0 \ub530\ub978 \ucd5c\uc801\uc758 \uc120\ud0dd \ubc29\ubc95 - \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\/40412\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"48. 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