Created
August 27, 2017 14:14
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A trained deep neural net capable of playing the OpenAI's cart pole game 😎
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| import gym | |
| import random | |
| import numpy as np | |
| import tflearn | |
| from tflearn.layers.core import input_data, dropout, fully_connected | |
| from tflearn.layers.estimator import regression | |
| from statistics import mean, median | |
| from collections import Counter | |
| LR = 1e-3 | |
| env = gym.make('CartPole-v0') | |
| env.reset() | |
| goal_steps = 500 | |
| score_requirement = 50 | |
| initial_games = 10000 | |
| def random_games(): | |
| for episode in range(5): | |
| env.reset() | |
| for t in range(goal_steps): | |
| env.render() | |
| action = env.action_space.sample() | |
| observation, reward, done, info = env.step(action) | |
| if done: | |
| break | |
| #random_games() | |
| def initial_population(): | |
| training_data = [] | |
| scores = [] | |
| accepted_scores = [] | |
| for _ in range(initial_games): | |
| score = 0 | |
| game_memory = [] | |
| prev_observation = [] | |
| for _ in range(goal_steps): | |
| action = random.randrange(0, 2) | |
| observation, reward, done, info = env.step(action) | |
| if len(prev_observation) > 0: | |
| game_memory.append([prev_observation, action]) | |
| prev_observation = observation | |
| score += reward | |
| if done: | |
| break | |
| if score >= score_requirement: | |
| accepted_scores.append(score) | |
| for data in game_memory: | |
| if data[1] == 1: | |
| output = [0,1] | |
| elif data[1] == 0: | |
| output = [1,0] | |
| training_data.append([data[0], output]) | |
| env.reset() | |
| scores.append(score) | |
| training_data_save = np.array(training_data) | |
| np.save('saved.npy', training_data_save) | |
| print('Average accepted scores:', mean(accepted_scores)) | |
| print('Median accepted score:', median(accepted_scores)) | |
| print('Counter accepted score:', Counter(accepted_scores)) | |
| return training_data | |
| def neural_network(input_size): | |
| net = input_data(shape=[None, input_size, 1], name='input') | |
| # Hidden layer 1 | |
| net = fully_connected(net, 128, activation='relu') | |
| net = dropout(net, 0.8) | |
| # Hidden layer 2 | |
| net = fully_connected(net, 256, activation='relu') | |
| net = dropout(net, 0.8) | |
| # Hidden layer 3 | |
| net = fully_connected(net, 512, activation='relu') | |
| net = dropout(net, 0.8) | |
| # Hidden layer 4 | |
| net = fully_connected(net, 256, activation='relu') | |
| net = dropout(net, 0.8) | |
| # hidden layer 5 | |
| net = fully_connected(net, 128, activation='relu') | |
| net = dropout(net, 0.8) | |
| # Output layer | |
| net = fully_connected(net, 2, activation='softmax') | |
| net = regression(net, optimizer='adam', learning_rate=LR, | |
| loss='categorical_crossentropy', name='target') | |
| model = tflearn.DNN(net, tensorboard_dir='log') | |
| return model | |
| def train_model(training_data, model=False): | |
| X = np.array([i[0] for i in training_data]).reshape(-1, len(training_data[0][0]), 1) | |
| y = [i[1] for i in training_data] | |
| if not model: | |
| model = neural_network(input_size=len(X[0])) | |
| model.fit({'input':X}, {'target':y}, n_epoch=3, snapshot_step=500, | |
| show_metric=True, run_id='openaigame') | |
| return model | |
| training_data = initial_population() | |
| model = train_model(training_data) | |
| scores = [] | |
| choices = [] | |
| for each_game in range(10): | |
| score = 0 | |
| game_memory = [] | |
| prev_obs = [] | |
| env.reset() | |
| for _ in range(goal_steps): | |
| env.render() | |
| if len(prev_obs) == 0: | |
| action = random.randrange(0, 2) | |
| else: | |
| action = np.argmax(model.predict(prev_obs.reshape(-1, len(prev_obs), 1))[0]) | |
| choices.append(action) | |
| new_obs, reward, done, info = env.step(action) | |
| prev_obs = new_obs | |
| game_memory.append([new_obs, action]) | |
| score += reward | |
| if done: | |
| break | |
| scores.append(score) | |
| print('Average Score:', sum(scores)/len(scores)) | |
| print('Choice 1: {}, Choice 2: {}'.format(choices.count(1)/len(choices), | |
| choices.count(0)/len(choices))) |
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