garage.sampler.vec_worker module

Worker that “vectorizes” environments.

class VecWorker(*, seed, max_path_length, worker_number, n_envs=8)[source]

Bases: garage.sampler.default_worker.DefaultWorker

Worker with a single policy and multiple environemnts.

Alternates between taking a single step in all environments and asking the policy for an action for every environment. This allows computing a batch of actions, which is generally much more efficient than computing a single action when using neural networks.

Parameters:
  • seed (int) – The seed to use to intialize random number generators.
  • max_path_length (int or float) – The maximum length paths which will be sampled. Can be (floating point) infinity.
  • 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.
DEFAULT_N_ENVS = 8
collect_rollout()[source]

Collect all completed rollouts.

Returns:
A batch of the trajectories completed since
the last call to collect_rollout().
Return type:garage.TrajectoryBatch
shutdown()[source]

Close the worker’s environments.

start_rollout()[source]

Begin a new rollout.

step_rollout()[source]

Take a single time-step in the current rollout.

Returns:True iff at least one of the paths was completed.
Return type:bool
update_agent(agent_update)[source]

Update an agent, assuming it implements garage.Policy.

Parameters:agent_update (np.ndarray or dict or garage.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(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 (gym.Env 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.