A General Approach of Automated Environment Design for Learning the Optimal Power Flow
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Reinforcement learning (RL) algorithms are increasingly used to solve the optimal power flow (OPF) problem. Yet, the question of how to design RL environments to maximize training performance remains unanswered, both for the OPF and the general case. We propose a general approach for automated RL environment design by utilizing multi-objective optimization. For that, we use the hyperparameter optimization (HPO) framework, which allows the reuse of existing HPO algorithms and methods. On five OPF benchmark problems, we demonstrate that our automated design approach consistently outperforms a manually created baseline environment design. Further, we use statistical analyses to determine which environment design decisions are especially important for performance, resulting in multiple novel insights on how RL-OPF environments should be designed. Finally, we discuss the risk of overfitting the environment to the utilized RL algorithm. To the best of our knowledge, this is the first general approach for automated RL environment design.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 20 of its 52 references that are in the map. First posted on arXiv 1 May 2025.
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- 2016Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?cited by 48
7 more
- 2020Action Space Shaping in Deep Reinforcement Learningcited by 46
- 2020Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learningcited by 27
- 2020Efficient Contextual Bandits with Continuous Actionscited by 24
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- 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 14
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- 2018Optimal Power Flow: An Introduction to Predictive, Distributed and Stochastic Control Challengescited by 1
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- 2024ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
- 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systems
- 2026Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency
- 2026Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization