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Reinforcement learning algorithms in RLlib

v0.8.6

3 years ago

New Features

  • (modules): add new DDPG and MBDDPG
  • (modules): add new SAC and MBSAC
  • (modules): add stochastic models with parallelized forward pass for ensembles
  • (modules): add stochastic policies and actor
  • (agents): use new DDPG and SAC modules as defaults
  • (agents): use new model-based DDPG and SAC modules as defaults
  • (policy): check model, action dist, and exploration compatibility
  • (policy): check module compatibility with action dist
  • (policy): add model property
  • (policy): add stochastic and deterministic action dist wrappers
  • (pytorch): add reverse mode to TanhSquash
  • (pytorch): add no-op initializer

Refactorings

  • rename raylab.losses to raylab.policy.losses
  • rename raylab.modules to raylab.policy.modules
  • (policy): remove TargetNetworksMixin
  • (tests): match test and package directory structures

Others

  • (examples): use MBSAC in MBPO
  • (examples): use MBSAC in MAPO
  • (modules): rename parameter_noise option to separate_behavior
  • (modules): move old NNs to v0 submodule
  • (policy): allow subclasses to set dist_class before calling init