garage.np.algos
¶
Reinforcement learning algorithms which use NumPy as a numerical backend.
- class CEM(env_spec, policy, sampler, n_samples, discount=0.99, init_std=1, best_frac=0.05, extra_std=1.0, extra_decay_time=100)¶
Bases:
garage.np.algos.rl_algorithm.RLAlgorithm
Cross Entropy Method.
CEM works by iteratively optimizing a gaussian distribution of policy.
In each epoch, CEM does the following: 1. Sample n_samples policies from a gaussian distribution of
mean cur_mean and std cur_std.
Collect episodes for each policy.
Update cur_mean and cur_std by doing Maximum Likelihood Estimation over the n_best top policies in terms of return.
- Parameters
env_spec (EnvSpec) – Environment specification.
policy (garage.np.policies.Policy) – Action policy.
sampler (garage.sampler.Sampler) – Sampler.
n_samples (int) – Number of policies sampled in one epoch.
discount (float) – Environment reward discount.
best_frac (float) – The best fraction.
init_std (float) – Initial std for policy param distribution.
extra_std (float) – Decaying std added to param distribution.
extra_decay_time (float) – Epochs that it takes to decay extra std.
- class CMAES(env_spec, policy, sampler, n_samples, discount=0.99, sigma0=1.0)¶
Bases:
garage.np.algos.rl_algorithm.RLAlgorithm
Covariance Matrix Adaptation Evolution Strategy.
Note
The CMA-ES method can hardly learn a successful policy even for simple task. It is still maintained here only for consistency with original rllab paper.
- Parameters
env_spec (EnvSpec) – Environment specification.
policy (garage.np.policies.Policy) – Action policy.
sampler (garage.sampler.Sampler) – Sampler.
n_samples (int) – Number of policies sampled in one epoch.
discount (float) – Environment reward discount.
sigma0 (float) – Initial std for param distribution.
- class MetaRLAlgorithm¶
Bases:
garage.np.algos.rl_algorithm.RLAlgorithm
,abc.ABC
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
- 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
- class NOP¶
Bases:
garage.np.algos.rl_algorithm.RLAlgorithm
NOP (no optimization performed) policy search algorithm.
- init_opt(self)¶
Initialize the optimization procedure.
- optimize_policy(self, paths)¶
Optimize the policy using the samples.