All of the things mentioned in the implemented section are not yet implemented in the refactored version. Hopefully will be done by the end of the weekend
Important Notes
This repository (rlswiss) has been extended from the August 2018 version of rlkit. Since then the design approaches of rlswiss and rlkit have deviated quite a bit, and it is for this reason that we are releasing rlswiss as a separate repository. If you find this repository useful for your research/projects, please cite this repository as well as rlkit.
rlswiss
Reinforcement Learning (RL) and Learning from Demonstrations (LfD) framework for the single task as well as meta-learning settings.
Our goal throughout has been to make it very efficient to implement new ideas quickly and cleanly. The core infrastructure is learning-framework-agnostic (PyTorch, Tf, etc.), however current implementations of specific algorithms are all in PyTorch.
Implemented RL algorithms:
- Soft-Actor-Critic (SAC)
Implemented LfD algorithms:
- Adversarial methods for Inverse Reinforcement Learning
- AIRL / GAIL / FAIRL / Discriminator-Actor-Critic
- Behaviour Cloning
- DAgger
Implemented Meta-RL algorithms:
- RL with observed task parameters
Implemented Meta-LfD algorithms:
- SMILe
- Meta Behaviour Cloning
- Meta DAgger