Multi-agent common knowledge reinforcement learning

Conference item

Cooperative multi-agent reinforcement learning often requires decentralised policies, which severely limit the agents' ability to coordinate their behaviour. In this paper, we show that common knowledge between agents allows for complex decentralised coordination. Common knowledge arises naturally in a large number of decentralised cooperative multi-agent tasks, for example, when agents can reconstruct parts of each others' observations. Since agents can independently agree on their common knowledge, they can execute complex coordinated policies that condition on this knowledge in a fully decentralised fashion. We propose multi-agent common knowledge reinforcement learning (MACKRL), a novel stochastic actor-critic algorithm that learns a hierarchical policy tree. Higher levels in the hierarchy coordinate groups of agents by conditioning on their common knowledge, or delegate to lower levels with smaller subgroups but potentially richer common knowledge. The entire policy tree can be executed in a fully decentralised fashion. As the lowest policy tree level consists of independent policies for each agent, MACKRL reduces to independently learnt decentralised policies as a special case. We demonstrate that our method can exploit common knowledge for superior performance on complex decentralised coordination tasks, including a stochastic matrix game and challenging problems in StarCraft II unit micromanagement.

Authors: de Witt, C; Foerster, J; Farquhar, G; Torr, PHS; Boehmer, W

• Alternative title: Conference paper
• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Massachusetts Institute of Technology Press
• Host title: Advances in Neural Information Processing Systems 32 (NIPS 2019)
• Journal: Neural Information Processing Systems
• Publication date: 2019-12-10
• Acceptance date: 2019-12-08
• Event title: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
• Event location: Vancouver, Canada
• Event start date: 2019-12-08
• Event end date: 2019-12-14
• ISSN: 1049-5258
• Language: English
• Keywords: FFR