MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning

Understand

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.