def test(env, agent, simulation_settings):
obs_size = env.observation_space.shape[0]
util = Utils()
state_samples = get_state_sample(samples=5000, normal_state=True)
util.create_normalizer(state_sample=state_samples)
for episode in range(1, simulation_settings['Episodes']):
done = False
total_reward = 0
state = env.reset()
state = util.normalize(state)
for i in range(1000): #
if simulation_settings['Render']:
#env.refresh(render=True)
env.render()
# infer an action
action = agent.get_action(np.reshape(state, (1, obs_size)),
explore=False)
# take it
state, reward, done, _ = env.step(action[0])
state = util.normalize(state)
total_reward += reward
if done:
break
agent.log_data(total_reward, episode)
print("Reward:\t{0}".format(total_reward))
def train(env, agent, FLAGS):
print(
"Fuel Cost = 0, Max Steps = 500, Episode Training = 2000, RANDOM FORCE = 20000, RANDOM X_FORCE = 0.2*RANDOM FORCE"
)
#print("Fuel Cost = 0, Max Steps = Unlimited, Episode Training = 2000")
obs_size = env.observation_space.shape[0]
util = Utils()
state_samples = get_state_sample(samples=5000, normal_state=True)
util.create_normalizer(state_sample=state_samples)
for episode in range(1, FLAGS.num_episodes + 1):
old_state = None
done = False
total_reward = 0
state = env.reset()
state = util.normalize(state)
max_steps = 500
left_or_right_barge_movement = np.random.randint(0, 2)
epsilon = 0.05
for t in range(max_steps): # env.spec.max_episode_steps
if FLAGS.show or episode % 10 == 0:
env.refresh(render=True)
old_state = state
# infer an action
action = agent.get_action(np.reshape(state, (1, obs_size)),
not FLAGS.test)
# take it
state, reward, done, _ = env.step(action[0])
state = util.normalize(state)
total_reward += reward
if state[LEFT_GROUND_CONTACT] == 0 and state[
RIGHT_GROUND_CONTACT] == 0:
env.move_barge_randomly(epsilon, left_or_right_barge_movement)
env.apply_random_x_disturbance(
epsilon=0.005, left_or_right=left_or_right_barge_movement)
env.apply_random_y_disturbance(epsilon=0.005)
if not FLAGS.test:
# update q vals
agent.update(old_state, action[0], np.array(reward), state,
done)
if done:
break
agent.log_data(total_reward, episode)
if episode % 50 == 0 and not FLAGS.test:
print('Saved model at episode', episode)
agent.save_model(episode)
print("Episode:\t{0}Reward:\t{1}".format(episode, total_reward))
def sample_state_and_create_normalizer(self):
from environments.rocketlander import get_state_sample
state_samples = get_state_sample(
samples=2000,
short_state=self.short_state_FLAG,
untransformed_state=self.untransformed_state_FLAG)
self.util.create_normalizer(state_sample=state_samples)