SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems
Christopher Yeh, Victor Li, Rajeev Datta, Julio Arroyo, Nicolas Christianson, Chi Zhang, Yize Chen, Mohammad Mehdi Hosseini
Abstract
The lack of standardized benchmarks for reinforcement learning (RL) in sustainability applications has made it difficult to both track progress on specific domains and identify bottlenecks for researchers to focus their efforts. In this paper, we present SustainGym, a suite of five environments designed to test the performance of RL algorithms on realistic sustainable energy system tasks, ranging from electric vehicle charging to carbon-aware data center job scheduling. The environments test RL algorithms under realistic distribution shifts as well as in multi-agent settings. We show that standard off-the-shelf RL algorithms leave significant room for improving performance and highlight the challenges ahead for introducing RL to real-world sustainability tasks.
BibTeX
@inproceedings{
yeh2023sustaingym,
title={SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems},
author={Christopher Yeh and Victor Li and Rajeev Datta and Julio Arroyo and Nicolas Christianson and Chi Zhang and Yize Chen and Mohammad Mehdi Hosseini and Azarang Golmohammadi and Yuanyuan Shi and Yisong Yue and Adam Wierman},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=vZ9tA3o3hr}
}