Improving Uncoordinated Collaboration in Partially Observable Domains with Imperfect Simultaneous Action Communication

Decentralised planning in partially observable multi-agent domains is limited by the interacting agents’ incomplete knowledge of their peers, which impacts their ability to work jointly towards a common goal. In this context, communication is often used as a means of observation exchange, which helps each agent in reducing uncertainty and acquiring a more centralised view of the world. However, despite these merits, planning with communicated observations is highly sensitive to communication channel noise and synchronisation issues, e.g. message losses, delays, and corruptions. In this paper, we propose an alternative approach to partially observable uncoordinated collaboration, where agents simultaneously execute and communicate their actions to their teammates. Our method extends a state-of-the-art Monte-Carlo planner for use in multi-agent systems, where communicated actions are incorporated directly in the sampling and learning process. We evaluate our approach in a benchmark multi-agent domain, and a more complex multi-robot problem with a larger action space. The experimental results demonstrate that our approach can lead to robust collaboration under challenging communication constraints and high noise levels, even in the presence of teammates who do not use any communication.