Promoting Diversity in Mixed Complex Cooperative and Competitive Multi-Agent Environment
Jia Wu, Z. Huang · 2023
This paper introduces a new approach for promoting diversity of behavior in complex multi-agent environments that pose three challenges: 1) competition or collaboration among agents of diverse types, 2) the need for complex multi-agent coordination, which makes it challenging to achieve risky cooperation strategies, and 3) a large number of agents in the environment, leading to increased complexity when considering agent-to-agent relationships. To address the first two challenges, we leverage Reward Randomization in combination with Bayesian Optimization to train agents to exhibit diverse strategic behaviors, thereby mitigating the issue of risky cooperation. To address the challenge of learning in a large number of agents, we utilize MAPPO with parameter sharing to enhance learning efficiency. Experimental results demonstrate that within this multi-agent environment, agents can effectively learn multiple visually distinct behaviors, and the incorporation of these two techniques significantly improves agents' performance.