`evorl.ec.optimizers.ars`¶

Module Contents¶

Classes¶

`ARS`	Augmented Random Search.
`ARSState`	State of the ARS.

API¶

class evorl.ec.optimizers.ars.ARS[source]¶

Bases: evorl.ec.optimizers.ec_optimizer.EvoOptimizer

Augmented Random Search.

Paper: Simple random search of static linear policies is competitive for reinforcement learning

ask(state: evorl.ec.optimizers.ars.ARSState) → tuple[evorl.types.Params, evorl.ec.optimizers.ec_optimizer.ECState][source]¶

fitness_std_eps: float¶: 1e-08

init(mean: evorl.types.Params, key: chex.PRNGKey) → evorl.ec.optimizers.ars.ARSState[source]¶

lr: float¶: None

noise_std: float¶: None

num_elites: int¶: None

optimizer: optax.GradientTransformation¶: ‘pytree_field(…)’

optimizer_name: str¶: ‘sgd’

pop_size: int¶: None

tell(state: evorl.ec.optimizers.ars.ARSState, fitnesses: chex.Array) → tuple[evorl.types.PyTreeDict, evorl.ec.optimizers.ars.ARSState][source]¶

class evorl.ec.optimizers.ars.ARSState[source]¶

Bases: evorl.types.PyTreeData

State of the ARS.

key: chex.PRNGKey¶: None

mean: chex.ArrayTree¶: None

noise: None | chex.ArrayTree¶: None

opt_state: optax.OptState¶: None