MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning

Tristan Tomilin; Luka van den Boogaard; Samuel Garcin; Constantin Ruhdorfer; Bram Grooten; Fabrice Kusters; Yali Du; Andreas Bulling; Mykola Pechenizkiy; Meng Fang

arXiv · 2506.14990

MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning

Tristan Tomilin·Luka van den Boogaard·Samuel Garcin·Constantin Ruhdorfer·Bram Grooten·Fabrice Kusters·Yali Du·Andreas Bulling·Mykola Pechenizkiy·Meng Fang

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Abstract

Benchmarks play a central role in reinforcement learning (RL) research, yet their computational constraints often shape what is studied. Despite the motivation of lifelong learning, most continual RL papers consider only 3-10 sequential tasks, as CPU-bound environments make longer sequences impractical. Meanwhile, continual learning in cooperative multi-agent settings remains largely unexplored. To address these gaps, we introduce MEAL (Multi-agent Environments for Adaptive Learning), the first benchmark for continual multi-agent RL. By leveraging JAX and GPU acceleration, MEAL enables training on sequences of 100 tasks in a few hours on a single GPU. We find that long task sequences reveal failure modes that do not appear at smaller scales.

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Tristan Tomilin, Luka van den Boogaard, Samuel Garcin, Constantin Ruhdorfer, Bram Grooten, Fabrice Kusters, Yali Du, Andreas Bulling, Mykola Pechenizkiy, Meng Fang. 2025-06-17. MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning. https://arxiv.org/abs/2506.14990

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