A General Approach of Automated Environment Design for Learning the Optimal Power Flow

TL;DR

This paper introduces a novel automated method for designing reinforcement learning environments for optimal power flow problems, improving training performance through multi-objective optimization and hyperparameter tuning.

Contribution

It presents the first general approach for automated RL environment design using multi-objective optimization and HPO, outperforming manual designs on benchmark problems.

Findings

Automated environment design consistently outperforms manual baseline.

Identifies key environment design factors influencing RL performance.

Provides insights into avoiding overfitting in environment design.

Abstract

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.