KERAS: Reinfrocement network

import numpy as np
import gym

from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Convolution2D
from keras.optimizers import RMSprop

from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory


ENV_NAME = 'CartPole-v0'

HLS = 100

# Setting the env up
env = gym.make(ENV_NAME)
n_action = env.action_space.n

# Build the neural network
model = Sequential()

model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
model.add((Dense(HLS)))
model.add(Activation('relu'))
model.add((Dense(HLS)))
model.add(Activation('relu'))
model.add((Dense(HLS)))
model.add(Activation('relu'))
model.add(Dense(n_action))
model.add(Activation('linear'))

print model.summary()  # How did it go? * ( ' ^')*

# Configure the RL agent

memory = SequentialMemory(limit=50000)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model, nb_actions=n_action, memory=memory, nb_steps_warmup=10,
               target_model_update=1e-2, policy=policy)

dqn.compile(optimizer=RMSprop(), metrics=['mae'])

dqn.fit(env, nb_steps=100000, verbose=2, visualize=False)

dqn.test(env, nb_episodes=5, visualize=True)