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| from __future__ import division | |
| import gym | |
| import numpy as np | |
| import tflearn | |
| from tflearn.data_utils import to_categorical | |
| from tflearn.layers.core import input_data, dropout, fully_connected | |
| from tflearn import lstm, embedding | |
| from tflearn.layers.estimator import regression | |
| import random | |
| from collections import deque | |
| NUM_ACTIONS = 2 | |
| NUM_STATES = 4 | |
| MAX_REPLAY_STATES = 100 | |
| NUM_GAMES_TRAIN = 200 | |
| def create_model(n_inputs, n_outputs): | |
| network = input_data(shape = [None, n_inputs]) | |
| network = fully_connected(network, 128, activation = 'relu') | |
| #network = dropout(network, 0.5) | |
| network = fully_connected(network, 256 , activation = 'relu') | |
| #network = dropout(network, 0.5) | |
| network = fully_connected(network, 128, activation = 'relu') | |
| #network = dropout(network, 0.5) | |
| network = fully_connected(network, n_outputs, activation = 'softmax') | |
| network = regression(network, | |
| optimizer = 'adam', | |
| loss = 'categorical_crossentropy') | |
| model = tflearn.DNN( | |
| network, | |
| max_checkpoints = 0, | |
| tensorboard_verbose = 0, | |
| tensorboard_dir = 'logs' | |
| ) | |
| return model | |
| env = gym.make('CartPole-v0') | |
| model = create_model(NUM_STATES, NUM_ACTIONS) | |
| replay = deque([]) | |
| gamma = 0.9 | |
| epsilon = 1 | |
| for number_game in range(NUM_GAMES_TRAIN): | |
| observation = env.reset() | |
| reward_game = 0 | |
| print '[+] Game ' + str(number_game) | |
| while True: | |
| env.render() | |
| q = model.predict([observation])[0] | |
| if random.random() < epsilon: | |
| action = np.random.randint(0, NUM_ACTIONS) | |
| else: | |
| action = np.argmax(q) | |
| new_state, reward, done, info = env.step(action) | |
| reward_game += reward | |
| replay.append((new_state, reward, action, done, observation)) | |
| if len(replay) > MAX_REPLAY_STATES: | |
| replay.popleft() # We take the oldest replay | |
| X_train = [] | |
| Y_train = [] | |
| for rep in replay: | |
| new_state, reward, action, done_rep, old_state = rep | |
| old_q = model.predict([old_state])[0] | |
| new_q = model.predict([new_state])[0] | |
| max_new_q = np.max(new_q) | |
| update_target = np.zeros(NUM_ACTIONS) | |
| update_target[:] = old_q[:] | |
| if done_rep: | |
| update = reward | |
| else: | |
| update = (reward + (gamma * max_new_q)) | |
| update_target[action] = update | |
| X_train.append(old_state) | |
| Y_train.append(update_target) | |
| model.fit( | |
| X_train, Y_train, | |
| validation_set = 0, | |
| n_epoch = 1, | |
| batch_size = MAX_REPLAY_STATES, | |
| shuffle = True, | |
| show_metric = False, | |
| snapshot_step = 200, | |
| snapshot_epoch = False, | |
| run_id = 'carpole_rl' | |
| ) | |
| if done or reward_game > 200: | |
| break | |
| print "[+] Game " + str(number_game) + " Reward " + str(reward_game) | |
| if epsilon > 0.1: | |
| epsilon -= (1 / 1000) | |
| env.monitor.close() | |
| model.save('model.tfl') | |
| #gym.upload( | |
| # '/tmp/cartpole-experiment-1', | |
| # writeup = 'https://gist.github.com/gdb/', | |
| # api_key = '' | |
| #) |
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