(ICML 2024) The official code for VEB-RL from Value-Evolutionary-Based Reinforcement Learning) by Pengyi Li.
VEB-RL is a hybrid framework specifically designed for value-based reinforcement learning methods. VEB-RL integrates genetic algorithms (GA) and cross-entropy method (CEM), using TD error as fitness for more accurate value function approximation. We also propose the Elite Interaction Mechanism to improve sample quality. VEB-RL significantly enhances value-based RL across various tasks.
Tip
🔥 🔥 🔥 If you are interested in ERL for policy search or other hybrid algorithms combining EA and RL, we strongly recommend reading our survey paper: Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms. It provides a comprehensive and accessible overview of research directions and classifications suitable for researchers with various backgrounds.
If you do find our paper or the repository helpful (or if you would be so kind as to offer us some encouragement), please consider kindly giving a star, and citing our paper.
@inproceedings{li2023value,
title={Value-Evolutionary-Based Reinforcement Learning},
author={Li, Pengyi and Jianye, HAO and Tang, Hongyao and Zheng, Yan and Barez, Fazl},
booktitle={Forty-first International Conference on Machine Learning},
year={2023}
}
You need to create a Weights & Biases account for visualizing results, and you should already have conda installed.
First, we create an environment based on the provided requirements.txt:
conda create --name VEBRL --file requirements.txt
Activate the environment:
conda activate VEBRL
Then enter either the GA_VEB folder or the CEM_VEB folder and directly run run.sh
cd ./GA_VEB or cd ./CEM_VEB
chmod 777 ./run.sh
./run.sh
The specific hyperparameter settings need to be adjusted according to the original paper.
VEB-RL is licensed under the MIT license.
For any questions, please feel free to email lipengyi@tju.edu.cn.