while not done:
if i < render_episodes:
# render for viewing experience
env.render()
obs_img = np.roll(obs_img, 1, axis=0)
obs_img[0, :] = observation
reshaped = obs_img.reshape((1, history_len * obs_len))
action = model.predict_move(reshaped, train=False)
# if the model is not initialized, take a random action instead
if action == None:
action = env.action_space.sample()
# use action to make a move
observation, reward, done, info = env.step(action)
cumulative_reward += reward
print('current average: {} in {} games'.format(
cumulative_reward / (i + 1), (i + 1)))
print('average score: {}'.format(cumulative_reward / eval_episodes))
return cumulative_reward / eval_episodes
if __name__ == "__main__":
# create model
model = nn.Control_Model()
EvalModel(model)
train_episodes = 200 # number of episodes before training
eval_episodes = 10 # number of episode when evaluating
render_episodes = 1 # number of episodes to render when evaluating
env = gym.make(env_name)
if enable_video:
env = gym.wrappers.Monitor(env,
directory=vid_dir,
force=False,
resume=True)
obs_len = len(env.observation_space.low)
act_len = env.action_space.n
# create model
model = nn.Control_Model(obs_len * historic_data_len, act_len)
train_count = 0
# train to until above the threshold
while (em.EvalModel(model, env, eval_episodes, render_episodes,
historic_data_len, obs_len) < trained_threshold):
max_reward = -np.inf
# run a segment of 200 'games' and train off of the max score
for i in range(train_episodes):
cumulative_reward = 0
obs_log = []
action_log = []
done = False
obs_img = np.zeros((historic_data_len, obs_len))
