garage.sampler.fragment_worker

Worker that “vectorizes” environments.

class FragmentWorker(*, seed, max_episode_length, worker_number, n_envs=DEFAULT_N_ENVS, timesteps_per_call=1)

Bases: garage.sampler.default_worker.DefaultWorker

Inheritance diagram of garage.sampler.fragment_worker.FragmentWorker

Vectorized Worker that collects partial episodes.

Useful for off-policy RL.

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.
  • of fragments before they're transmitted out of (length) –
  • worker_number (int) – The number of the worker this update is occurring in. This argument is used to set a different seed for each worker.
  • n_envs (int) – Number of environment copies to use.
  • timesteps_per_call (int) – Maximum number of timesteps to gather per env per call to the worker. Defaults to 1 (i.e. gather 1 timestep per env each call, or n_envs timesteps in total each call).
DEFAULT_N_ENVS = 8
update_env(self, env_update)

Update the environments.

If passed a list (inside this list passed to the Sampler itself), distributes the environments across the “vectorization” dimension.

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.
  • ValueError – If the wrong number of updates is passed.
start_episode(self)

Resets all agents if the environment was updated.

step_episode(self)

Take a single time-step in the current episode.

Returns:True iff at least one of the episodes was completed.
Return type:bool
collect_episode(self)

Gather fragments from all in-progress episodes.

Returns:A batch of the episode fragments.
Return type:EpisodeBatch
rollout(self)

Sample a single episode of the agent in the environment.

Returns:The collected episode.
Return type:EpisodeBatch
shutdown(self)

Close the worker’s environments.

worker_init(self)

Initialize a worker.

update_agent(self, agent_update)

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.