SB3 v1.6.0: Huggingface hub integration, Recurrent PPO (PPO LSTM)

Release 1.6.0 (2022-08-05)

Breaking Changes

• Change default value for number of hyperparameter optimization trials from 10 to 500. (@ernestum)
• Derive number of intermediate pruning evaluations from number of time steps (1 evaluation per 100k time steps.) (@ernestum)
• Updated default --eval-freq from 10k to 25k steps
• Update default horizon to 2 for the HistoryWrapper
• Upgrade to Stable-Baselines3 (SB3) >= 1.6.0
• Upgrade to sb3-contrib >= 1.6.0

New Features

• Support setting PyTorch's device with thye --device flag (@Gregwar)
• Add --max-total-trials parameter to help with distributed optimization. (@ernestum)
• Added vec_env_wrapper support in the config (works the same as env_wrapper)
• Added Huggingface hub integration
• Added RecurrentPPO support (aka ppo_lstm)
• Added autodownload for "official" sb3 models from the hub
• Added Humanoid-v3, Ant-v3, Walker2d-v3 models for A2C (@pseudo-rnd-thoughts)
• Added MsPacman models

Bug fixes

• Fix Reacher-v3 name in PPO hyperparameter file
• Pinned ale-py==0.7.4 until new SB3 version is released
• Fix enjoy / record videos with LSTM policy
• Fix bug with environments that have a slash in their name (@ernestum)
• Changed optimize_memory_usage to False for DQN/QR-DQN on Atari games,
  if you want to save RAM, you need to deactivate handle_timeout_termination
  in the replay_buffer_kwargs

Documentation

Other

• When pruner is set to "none", use NopPruner instead of diverted MedianPruner (@qgallouedec)