Unifying Multi-Agent Reinforcement Learning through Standardization

This chapter delves into a capacity model designed for enhanced control and optimization in multi-agent reinforcement learning. It emphasizes the importance of reducing fragmentation by developing a cohesive library for algorithm and task customization, along with the introduction of benchmarks to facilitate research comparisons.