Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
This addresses the challenge of robust coordination transfer in multi-agent systems, which is incremental as it builds on existing transfer learning methods by incorporating coordination knowledge.
The paper tackled the problem of transferring coordination knowledge in cooperative multi-agent reinforcement learning, proposing a new architecture that achieved superior performance in StarCraft II experiments compared to state-of-the-art baselines.
Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination indispensable in cooperative MARL constrains each agent's policy. However, existing transfer methods focus exclusively on agent policy and ignores coordination knowledge. We propose a new architecture that realizes robust coordination knowledge transfer through appropriate decomposition of the overall coordination into several coordination patterns. We use a novel mixing network named level-adaptive QTransformer (LA-QTransformer) to realize agent coordination that considers credit assignment, with appropriate coordination patterns for different agents realized by a novel level-adaptive Transformer (LA-Transformer) dedicated to the transfer of coordination knowledge. In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios. Extensive experiments in StarCraft II micro-management show that LA-QTransformer together with PIT achieves superior performance compared with state-of-the-art baselines.