arXiv AI
The Role of Network Topology and Opponent Information in Shaping Cooperation in Multi-Agent Reinforcement Learning Systems
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Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learning
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Jul 13
Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning
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Emergence of cooperation: A reputation-modulated reinforcement learning
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Certifying cooperation: a novel approach to cooperative multi-agent task generation
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Fully Byzantine-Resilient Multi-Agent Reinforcement Learning
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Continual Graph Multi-Agent Reinforcement Learning
The paper introduces Continual Graph Multi-Agent Reinforcement Learning (CGMARL), a framework that maps sequences of tasks into attributed graphs to capture task-specific structures such as network topologies or formation arrangements. It presents the Graph-based Formation (GRAFO) benchmark to illustrate forgetting in graph-based CMARL settings and proposes the Frozen Graph Encoder (FROG) to preserve past structural information using a frozen graph backbone. Experiments on GRAFO demonstrate that combining FROG with existing continual learning methods significantly improves performance across multiple CGMARL scenarios.