##Link to Youtube Video : https://www.youtube.com/watch?v=uwz8JzrEwWY
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import time, random, math | |
import numpy as np | |
import gym | |
def uploadSimulation(): | |
API_KEY = open('/home/dollarakshay/Documents/API Keys/Open AI Key.txt', 'r').read().rstrip() | |
gym.upload('Artificial Intelligence/'+GAME, api_key=API_KEY) | |
def updateQTable(prevState, prevAction, reward, curState): |
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import time, random, math | |
import numpy as np | |
import gym | |
def uploadSimulation(): | |
API_KEY = open('/home/dollarakshay/Documents/API Keys/Open AI Key.txt', 'r').read().rstrip() | |
gym.upload('Artificial Intelligence/'+GAME, api_key=API_KEY) | |
def updateQTable(prevState, prevAction, reward, curState): |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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import time, random, math | |
import numpy as np | |
import gym | |
def uploadSimulation(): | |
API_KEY = open('/home/dollarakshay/Documents/API Keys/Open AI Key.txt', 'r').read().rstrip() | |
gym.upload('Artificial Intelligence/'+GAME, api_key=API_KEY) | |
def updateQTable(prevState, prevAction, reward, curState): |
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import time, math, random, bisect | |
import gym | |
import numpy as np | |
def sigmoid(x): | |
return 1.0/(1.0 + np.exp(-x)) | |
class NeuralNet : | |
def __init__(self, nodeCount): |
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import time, math, random, bisect, copy | |
import gym | |
import numpy as np | |
class NeuralNet : | |
def __init__(self, nodeCount): | |
self.fitness = 0.0 | |
self.nodeCount = nodeCount |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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import time, math, random, bisect | |
import gym | |
import numpy as np | |
def sigmoid(x): | |
return 1.0/(1.0 + np.exp(-x)) | |
class NeuralNet : | |
def __init__(self, nodeCount): |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import time, math, random, bisect | |
import gym | |
import numpy as np | |
def sigmoid(x): | |
return 1.0/(1.0 + np.exp(-x)) | |
class NeuralNet : | |
def __init__(self, nodeCount): |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import time, math, random, bisect | |
import gym | |
import numpy as np | |
def sigmoid(x): | |
return 1.0/(1.0 + np.exp(-x)) | |
class NeuralNet : | |
def __init__(self, nodeCount): |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import time, math, random, bisect, copy | |
import gym | |
import numpy as np | |
class NeuralNet : | |
def __init__(self, nodeCount): | |
self.fitness = 0 | |
self.nodeCount = nodeCount |
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