garage.sampler.default_worker module

Default Worker class.

class DefaultWorker(*, seed, max_path_length, worker_number)

Bases: garage.sampler.worker.Worker

Initialize a worker.

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 where this update is occurring. This argument is used to set a different seed for each worker.

agent

The worker’s agent.

Type:Policy or None

env

The worker’s environment.

Type:gym.Env or None

collect_rollout()

Collect the current rollout, clearing the internal buffer.

Returns:

A batch of the trajectories completed since

the last call to collect_rollout().

Return type:garage.TrajectoryBatch

rollout()

Sample a single rollout of the agent in the environment.

Returns:The collected trajectory. Return type:garage.TrajectoryBatch

shutdown()

Close the worker’s environment.

start_rollout()

Begin a new rollout.

step_rollout()

Take a single time-step in the current rollout.

Returns:True iff the path is done, either due to the environment indicating termination of due to reaching max_path_length. Return type:bool

update_agent(agent_update)

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)

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.

worker_init()

Initialize a worker.