MAMGDT: Enhancing Multi-Agent Systems with Multi-Game Decision Transformer

Multi-agent systems (MAS) introduce the necessity about achieving collaborative goals and individual decision quality simultaneously. In the subfield of multi-agent reinforcement learning, transformer-based methods like MGDT enabled transportable utilization of temporal contexts in decision making for single agents. We introduces Multi-Agent Multi-Game Decision Transformer (MAMGDT) as a workaround for MAS tasks which presents a in-context learning approach to enhance decision-making in complex multi-agent environments. By incorporating causal masking and agent-wise interaction modeling, MAMGDT maintains temporal accuracy and captures the dynamics of agent interactions effectively. With the potential to optimize decision processes, MAMGDT is well-suited for various multi-agent tasks. We carried out demostration of MAMGDT on popular SMACv1 benchmarks with both collaborative and adversarial tasks, where MAMGDT outperformed other approaches.