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.

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
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
train(self, runner)

Obtain samplers and start actual training for each epoch.

Parameters:runner (LocalRunner) – LocalRunner is passed to give algorithm the access to runner.step_epochs(), which provides services such as snapshotting and sampler control.