garage.tf.algos.te module¶

Task Embedding Algorithm.

class TaskEmbeddingWorker(*, seed, max_path_length, worker_number)[source]¶

Bases: garage.sampler.default_worker.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_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()[source]¶

Collect the current rollout, 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 trajectories completed since the last call to collect_rollout().
Return type:	garage.TrajectoryBatch

start_rollout()[source]¶: Begin a new rollout.

step_rollout()[source]¶

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