garage.np.algos.meta_rl_algorithm

Interface of Meta-RL ALgorithms.

class MetaRLAlgorithm

Bases: garage.np.algos.rl_algorithm.RLAlgorithm, abc.ABC

Inheritance diagram of garage.np.algos.meta_rl_algorithm.MetaRLAlgorithm

Base class for Meta-RL Algorithms.

abstract get_exploration_policy(self)

Return a policy used before adaptation to a specific task.

Each time it is retrieved, this policy should only be evaluated in one task.

Returns

The policy used to obtain samples, which are later used for

meta-RL adaptation.

Return type

Policy

abstract adapt_policy(self, exploration_policy, exploration_episodes)

Produce a policy adapted for a task.

Parameters

  • exploration_policy (Policy) – A policy which was returned from get_exploration_policy(), and which generated exploration_trajectories by interacting with an environment. The caller may not use this object after passing it into this method.

  • exploration_episodes (EpisodeBatch) – Episodes with which to adapt. These are generated by exploration_policy while exploring the environment.

Returns

A policy adapted to the task represented by the

exploration_episodes.

Return type

Policy

abstract train(self, trainer)

Obtain samplers and start actual training for each epoch.

Parameters

trainer (Trainer) – Trainer is passed to give algorithm the access to trainer.step_epochs(), which provides services such as snapshotting and sampler control.