def perform_expert_episodes_continuous(simu, batch, nb_trajs, render=False):
"""
Build a batch of 20 expert episodes playing a simple policy close to bangbang (go left first, then go right)
:param simu: the simulation
:param batch: the batch to be filled
:param render: whether the step is displayed or not (True or False)
:return: the batch
"""
for e in range(nb_trajs):
state = simu.reset(render)
episode = Episode()
for _ in range(50):
variation = random.random() / 20
action = [-1.0 + variation]
state, reward, done = simu.take_step(state, action, episode,
render)
for t in count():
variation = random.random() / 10
action = [1.0 - variation]
state, reward, done = simu.take_step(state, action, episode,
render)
if done:
batch.add_episode(episode)
# print("expert continuous nb steps:", t+50)
break
return batch
def train_on_one_episode(self, policy, deterministic, render=False):
"""
Perform an episode using the policy parameter and return the corresponding samples into an episode structure
:param policy: the policy controlling the agent
:param deterministic: whether the evaluation should use a deterministic policy or not
:param render: whether the episode is displayed or not (True or False)
:return: the samples stored into an episode
"""
episode = Episode()
state = self.reset(render)
for _ in count():
action = policy.select_action(state, deterministic)
next_state, _, done = self.take_step(state, action, episode,
render)
state = next_state
if done:
return episode