MARL2Grid-TR: A Multi-Agent RL Benchmark in Power Grid Operations

Enrico Marchesini, Eva Boguslawski, Alessandro Leite, Christopher Amato, Matthieu DUSSARTRE, Marc Schoenauer, Benjamin Donnot, Priya L. Donti

Keywords: Multi-agent reinforcement learning, benchmark, power grids

TL;DR: We introduce MARL2Grid, the first benchmark for multi-agent reinforcement learning in realistic power grid operations.

Abstract:

Improving power grid operations is essential for enhancing flexibility and accelerating grid decarbonization. Reinforcement learning (RL) has shown promise in this domain, most notably through the Learning to Run a Power Network (L2RPN) competition series, but prior work has primarily focused on single-agent settings, neglecting the often decentralized, multi-agent nature of grid control.
We fill this gap with MARL2Grid-TR, the first multi-agent RL (MARL) benchmark for grid topology and redispatching, developed in collaboration with transmission system operators. Built on RTE France’s high-fidelity simulation platform, our benchmark supports decentralized control across substations and generators, with configurable agent scopes, observability settings, expert-informed heuristics, and safety-critical constraints.
The benchmark includes a suite of realistic scenarios that expose key challenges, such as coordination under partial information, long-horizon objectives, and adherence to hard physical constraints. Empirical results show that current MARL methods struggle under these real-world conditions. By providing a standardized, extensible platform, we aim to advance the development of scalable, cooperative, and safe learning algorithms for power grids.

Primary Area: datasets and benchmarks

Submission Number: 11114