[Using Keras Reinforcement Learning API with OPENAI GYM] #gym #강화학습

RL.py

import gym
import random
import numpy as np
from keras.layers import Dense, Flatten
from keras.models import Sequential
from keras.optimizers import Adam

env = gym.make('CartPole-v1')

states = env.observation_space.shape[0]
print('States', states)


actions = env.action_space.n
print('Actions', actions)


episodes = 10

for episode in range(1,episodes+1):
	# At each begining reset the game 
	state = env.reset()
	# set done to False
	done = False
	# set score to 0
	score = 0
	# while the game is not finished
	while not done:
		# visualize each step
		env.render()
		# choose a random action
		action = random.choice([0,1])
		# execute the action
		n_state, reward, done, info = env.step(action)
		# keep track of rewards
		score+=reward
	print('episode {} score {}'.format(episode, score))

def agent(states, actions):
	model = Sequential()
	model.add(Flatten(input_shape = (1, states)))
	model.add(Dense(24, activation='relu'))
	model.add(Dense(24, activation='relu'))
	model.add(Dense(24, activation='relu'))
	model.add(Dense(actions, activation='linear'))
	return model

model = agent(env.observation_space.shape[0], env.action_space.n)

from rl.agents import SARSAAgent
from rl.policy import EpsGreedyQPolicy

policy = EpsGreedyQPolicy()

sarsa = SARSAAgent(model = model, policy = policy, nb_actions = env.action_space.n)

sarsa.compile('adam', metrics = ['mse'])

sarsa.fit(env, nb_steps = 50000, visualize = False, verbose = 1)


scores = sarsa.test(env, nb_episodes = 100, visualize= False)
print('Average score over 100 test games:{}'.format(np.mean(scores.history['episode_reward'])))


sarsa.save_weights('sarsa_weights.h5f', overwrite=True)

# sarsa.load_weights('sarsa_weights.h5f')


_ = sarsa.test(env, nb_episodes = 2, visualize= True)