garage.tf.algos.te

Task Embedding Algorithm.

class TaskEmbeddingWorker(*, seed, max_episode_length, worker_number)

Bases: garage.sampler.DefaultWorker

A sampler worker for Task Embedding Algorithm.

In addition to DefaultWorker, this worker adds one-hot task id to env_info, and adds latent and latent infos to agent_info.

Parameters

• seed (int) – The seed to use to intialize random number generators.

• max_episode_length (int or float) – The maximum length of episodes to sample. 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

Environment or None

start_episode()

Begin a new episode.

step_episode()

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

bool

collect_episode()

Collect the current episode, clearing the internal buffer.

One-hot task id is saved in env_infos[‘task_onehot’]. Latent is saved in agent_infos[‘latent’]. Latent infos are saved in agent_infos[‘latent_info_name’], where info_name is the original latent info name.

Returns

A batch of the episodes completed since the last call

to collect_episode().

Return type

EpisodeBatch

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.

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 (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.

rollout()

Sample a single episode of the agent in the environment.

Returns

The collected episode.

Return type

EpisodeBatch

shutdown()

Close the worker’s environment.