def step_episode(self):
"""Take a single time-step in the current episode.
Returns:
bool: True iff the episode is done, either due to the environment
indicating termination of due to reaching `max_episode_length`.
"""
if self._eps_length < self._max_episode_length:
a, agent_info = self.agent.get_action(self._prev_obs)
if self._deterministic:
a = agent_info['mean']
a, agent_info = self.agent.get_action(self._prev_obs)
es = self.env.step(a)
self._observations.append(self._prev_obs)
self._env_steps.append(es)
for k, v in agent_info.items():
self._agent_infos[k].append(v)
self._eps_length += 1
if self._accum_context:
s = TimeStep.from_env_step(env_step=es,
last_observation=self._prev_obs,
agent_info=agent_info,
episode_info=self._episode_info)
self.agent.update_context(s)
if not es.last:
self._prev_obs = es.observation
return False
self._lengths.append(self._eps_length)
self._last_observations.append(self._prev_obs)
return True
def test_from_env_step_time_step(sample_data):
agent_info = sample_data['agent_info']
last_observation = sample_data['observation']
observation = sample_data['next_observation']
time_step = TimeStep(**sample_data)
del sample_data['agent_info']
del sample_data['next_observation']
sample_data['observation'] = observation
env_step = EnvStep(**sample_data)
time_step_new = TimeStep.from_env_step(env_step=env_step,
last_observation=last_observation,
agent_info=agent_info)
assert time_step == time_step_new