garage.np.algos.cma_es
¶
Covariance Matrix Adaptation Evolution Strategy.
-
class
CMAES
(env_spec, policy, baseline, 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.
baseline (garage.np.baselines.Baseline) – Baseline for GAE (Generalized Advantage Estimation).
n_samples (int) – Number of policies sampled in one epoch.
discount (float) – Environment reward discount.
sigma0 (float) – Initial std for param distribution.
-
train
(self, trainer)¶ Initialize variables and start training.