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PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
39 commits 18 branches 32 tags 5.2 MiB
Python 99.6%
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Antonin RAFFIN 7627a8644c Add roadmap
2019-09-22 13:43:01 +02:00
scripts Bug fixes + add evaluate script 2019-09-06 10:44:55 +02:00
tests Reformat 2019-09-21 17:17:09 +02:00
torchy_baselines Change default dist to gaussian 2019-09-22 12:56:27 +02:00
.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 Add roadmap 2019-09-22 13:43:01 +02:00
setup.cfg Bug fixes + add evaluate script 2019-09-06 10:44:55 +02:00
setup.py PPO VecEnv compat 2019-09-20 15:19:04 +02:00

README.md

Torchy-Baselines

TODO:

  • SAC
  • save/load
  • automatic choice for action distribution
  • predict
  • better rescale (min + action * range)
  • documentation
  • flexible mlp
  • logger
  • better monitor wrapper?

Later:

  • get_parameters / set_parameters
  • CNN policies + normalization
  • tensorboard support
  • DQN
  • TRPO
  • A2C
  • ACER
  • HER -> use stable-baselines because does not depends on tf?