Dynamic Conservative Degree Allocation for Offline Multi-Agent Reinforcement Learning

Offline Multi-agent Reinforcement Learning (MARL) has been designed to learn policies from pre-collected datasets without real-time interaction in multi-agent systems. A primary concern in offline MARL is the conservative degree allocation, which involves assigning different conservatism levels to agents based on their varying influence on the system. Current approaches frequently neglect this crucial aspect, resulting in suboptimal performance, particularly when agents have differing impacts on the environment. In this paper, we propose OMCDA, a novel offline MARL algorithm that addresses the issue of conservative degree allocation by assigning dynamic conservatism levels to each agent based on their individual influence on system performance. OMCDA decomposes the Q-function into two components: one for computing the return and another for capturing deviations from the behavior policy. Additionally, OMCDA employs a dynamic allocation mechanism that adjusts conservatism levels for agents based on varying impacts, while maintaining coherent credit assignment and ensuring robust system performance throughout learning. We evaluate OMCDA on MuJoCo and SMAC, showing it outperforms existing offline MARL methods in challenging tasks by effectively addressing conservative degree allocation.