arXiv:2506.14990v3 Announce Type: replace 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 lever
The increasing complexity and computational power available for AI research are enabling the exploration of more advanced and realistic learning paradigms, like continual multi-agent reinforcement learning, moving beyond simpler single-task or limited sequential task settings.
This benchmark addresses a critical gap in AI research by enabling more systematic investigation into lifelong learning for cooperative multi-agent systems, which is essential for developing robust and adaptive AI agents in dynamic real-world environments.
The introduction of MEAL provides a standardized, computationally feasible platform for researchers to develop and evaluate algorithms for continual multi-agent reinforcement learning, accelerating progress in a previously underexplored and computationally constrained area.
- · AI researchers
- · Reinforcement learning platforms
- · Developers of autonomous systems
- · Research limited to single-task RL
- · AI systems lacking adaptivity
Researchers gain a powerful tool to advance continual multi-agent RL, leading to more generalizable and adaptive AI.
Improved continual learning capabilities in multi-agent systems will accelerate the deployment of intelligent AI agents in complex, unstructured environments.
The development of highly adaptive multi-agent AI could significantly disrupt sectors reliant on human coordination or require dynamic resource allocation.
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Read at arXiv cs.AI