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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 · Azarang Golmohammadi · Yuanyuan Shi · Yisong Yue · Adam Wierman

Tue Dec 12 08:45 AM -- 10:45 AM (PST) @ Great Hall & Hall B1+B2 #1310
Event URL: https://chrisyeh96.github.io/sustaingym »

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.

Author Information

Christopher Yeh (California Institute of Technology)
Victor Li (California Institute of Technology)
Rajeev Datta (California Institute of Technology)
Julio Arroyo (California Institute of Technology)
Nicolas Christianson (California Institute of Technology)
Chi Zhang (University of California, San Diego)
Yize Chen (University of Washington)
Mohammad Mehdi Hosseini (University of Utah)
Azarang Golmohammadi (Beyond Limits, Inc.)
Yuanyuan Shi (University of California San Diego)

Yuanyuan Shi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego. Previously, she was a postdoc in the Computing and Mathematical Sciences Department at Caltech from 2020-2021 and finished her Ph.D. in Electrical and Computer Engineering from the University of Washington Seattle. Her research focuses on energy systems using machine learning, optimization and control.

Yisong Yue (Caltech)
Adam Wierman (Caltech)

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