COMAD discovers and reuses coordination skills from mixed offline MARL data via auto-encoders and density-based estimation to achieve continual learning with better transfer.
arXiv preprint arXiv:2506.14990 , year=
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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.
verdicts
UNVERDICTED 3representative citing papers
KNIFE targets stagnant neurons in MARL value factorization by replacing them with a composite of frozen, re-initialized, and compensating units to restore plasticity while preserving cooperation knowledge.
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.
citing papers explorer
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Offline Multi-agent Continual Cooperation via Skill Partition and Reuse
COMAD discovers and reuses coordination skills from mixed offline MARL data via auto-encoders and density-based estimation to achieve continual learning with better transfer.
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Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods
KNIFE targets stagnant neurons in MARL value factorization by replacing them with a composite of frozen, re-initialized, and compensating units to restore plasticity while preserving cooperation knowledge.
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.