Paper Title: Bootstrap Advantage Estimation for Policy Optimization in Reinforcement Learning. (ICMLA 2022). arXiv
This codebase is based on the CleanRL library. Please follow the installation instructions on the official library site. The code should run on version CleanRL v1.0.0b1. It might work on the latest version; however, some modifications might be needed.
The Procgen environments are based on image-based observation, and the policy and value networks use CNN-based models. Thus it is recommended to run those experiments on a GPU-enabled machine.
The experiments on DeepMind Control and PyBullet environments should run on the CPU.
For DeepMind Control environments, we use dmc2gym to convert the environments to OpenAI Gym format to be compatible with CleanRL. Please follow the instruction in the original repository for installation. A copy of the repository is provided here as well.
Please contact the author at [email protected] if you have any queries.
If you use this code or data, please consider citing this paper:
@inproceedings{rahman2022bae,
title={Bootstrap Advantage Estimation for Policy Optimization in Reinforcement Learning},
author={Rahman, Md Masudur and Xue, Yexiang},
booktitle={IEEE International Conference on Machine Learning and Applications (ICMLA 2022)},
year={2022}
}