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)[source]¶
Bases:
garage.sampler.default_worker.DefaultWorker
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 (length of fragments before they're transmitted out) –
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(env_update)[source]¶
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.
- step_episode()[source]¶
Take a single time-step in the current episode.
- Returns
True iff at least one of the episodes was completed.
- Return type
- collect_episode()[source]¶
Gather fragments from all in-progress episodes.
- Returns
A batch of the episode fragments.
- Return type
- rollout()[source]¶
Sample a single episode of the agent in the environment.
- Returns
The collected episode.
- Return type
- worker_init()¶
Initialize a worker.
- update_agent(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.