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PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
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Antonin Raffin 3cdd5f20af Bug fix + add test for sde net arch
2019-12-02 14:14:48 +01:00
docs Add doc 2019-09-26 11:46:40 +02:00
scripts Bug fixes + add evaluate script 2019-09-06 10:44:55 +02:00
tests Bug fix + add test for sde net arch 2019-12-02 14:14:48 +01:00
torchy_baselines Bug fix + add test for sde net arch 2019-12-02 14:14:48 +01:00
.coveragerc Bug fixes + add evaluate script 2019-09-06 10:44:55 +02:00
.gitignore Refactor: CEM-RL closer to TD3 implementation 2019-09-09 13:43:46 +02:00
LICENSE Init: TD3 2019-09-05 17:29:41 +02:00
README.md Log more values 2019-11-26 17:44:06 +01:00
setup.cfg Add flexible mlp 2019-10-17 13:32:25 +02:00
setup.py Bump version and change default noise clipping 2019-11-08 14:59:42 +01:00

README.md

Build Status Documentation Status

Torchy Baselines

PyTorch version of Stable Baselines, a set of improved implementations of reinforcement learning algorithms.

Implemented Algorithms

  • A2C
  • CEM-RL (with TD3)
  • PPO
  • SAC
  • TD3

Roadmap

TODO:

  • save/load

  • better predict

  • complete logger

  • Refactor: buffer with numpy array instead of pytorch

  • Refactor: remove duplicated code for evaluation

  • plotting? -> zoo

Later:

  • get_parameters / set_parameters
  • SDE: use affine transform to scale the noise after a tanh transform?
  • CNN policies + normalization
  • tensorboard support
  • DQN
  • TRPO
  • ACER
  • DDPG
  • HER -> use stable-baselines because does not depends on tf?