ROMA: Multi-Agent Reinforcement Learning with Emergent Roles

Autor:	Wang, Tonghan, Dong, Heng, Lesser, Victor, Zhang, Chongjie
Rok vydání:	2020
Předmět:	Computer Science - Multiagent Systems
Druh dokumentu:	Working Paper
Popis:	The role concept provides a useful tool to design and understand complex multi-agent systems, which allows agents with a similar role to share similar behaviors. However, existing role-based methods use prior domain knowledge and predefine role structures and behaviors. In contrast, multi-agent reinforcement learning (MARL) provides flexibility and adaptability, but less efficiency in complex tasks. In this paper, we synergize these two paradigms and propose a role-oriented MARL framework (ROMA). In this framework, roles are emergent, and agents with similar roles tend to share their learning and to be specialized on certain sub-tasks. To this end, we construct a stochastic role embedding space by introducing two novel regularizers and conditioning individual policies on roles. Experiments show that our method can learn specialized, dynamic, and identifiable roles, which help our method push forward the state of the art on the StarCraft II micromanagement benchmark. Demonstrative videos are available at https://sites.google.com/view/romarl/. Comment: Thirty-seventh International Conference on Machine Learning (ICML 2020)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.08039 Zobrazit plný text záznamu View this record from Arxiv