garage.sampler.default_worker¶
Default Worker class.
-
class
DefaultWorker(*, seed, max_episode_length, worker_number)[source]¶ Bases:
garage.sampler.worker.Worker
Initialize a worker.
- Parameters
seed (int) – The seed to use to intialize random number generators.
max_episode_length (int or float) – The maximum length of episodes which will be sampled. Can be (floating point) infinity.
worker_number (int) – The number of the worker where this update is occurring. This argument is used to set a different seed for each worker.
-
env¶ The worker’s environment.
- Type
Environment or None
-
update_agent(self, agent_update)[source]¶ Update an agent, assuming it implements
Policy.- Parameters
agent_update (np.ndarray or dict or Policy) – If a tuple, dict, or np.ndarray, these should be parameters to agent, which should have been generated by calling Policy.get_param_values. Alternatively, a policy itself. Note that other implementations of Worker may take different types for this parameter.
-
update_env(self, env_update)[source]¶ Use any non-None env_update as a new environment.
A simple env update function. If env_update is not None, it should be the complete new environment.
This allows changing environments by passing the new environment as env_update into obtain_samples.
- Parameters
env_update (Environment or EnvUpdate or None) – The environment to replace the existing env with. Note that other implementations of Worker may take different types for this parameter.
- Raises
TypeError – If env_update is not one of the documented types.
-
step_episode(self)[source]¶ Take a single time-step in the current episode.
- Returns
True iff the episode is done, either due to the environment indicating termination of due to reaching max_episode_length.
- Return type
-
collect_episode(self)[source]¶ Collect the current episode, clearing the internal buffer.
- Returns
- A batch of the episodes completed since the last call
to collect_episode().
- Return type