Discovering reinforcement learning algorithms

A jax/stax implementation of the NeurIPS 2020 paper: Discovering reinforcement learning algorithms [1]

The agent at lpg.agent.py implements the bsuite.baseline.base.Agent interface. The lpg/environments/*.py interfaces with a dm_env.Environment. We wrap the gym-atari suite using the bsuite.utils.gym_wrapper.DMEnvFromGym adapter into a dqn.AtariEnv to implement historical observations and actions repeat.

Installation

To run the algorithm on a GPU, I suggest to install the gpu version of jax [4]. You can then install this repo using Anaconda python and pip.

conda env create -n lpg
conda activate lpg
pip install git+https://github.com/epignatelli/discovering-reinforcement-learning-algorithms

References

[1] Oh, J., Hessel, M., Czarnecki, W.M., Xu, Z., van Hasselt, H.P., Singh, S. and Silver, D., 2020. Discovering reinforcement learning algorithms. Advances in Neural Information Processing Systems, 33.