Multi-Agent Online Coordination via Bayesian Inverse Planning

Abstract

Multi-agent coordination is a classical applied engineering problem and has been widely used in daily life. To efficiently coordinate all agents in the environment, most traditional models rely on a robust communication system between agents. However, in the scenario of autonomous vehicles, building a reliable communication system is extremely expensive and is very difficult to achieve in the foreseeable future. In this paper, we propose an online multi-agent coordination mechanism for each agent in the absence of communication and explore the benefits of Bayesian inverse planning in achieving the Nash equilibrium which provides an optimal coordination solution for each participant in multi-agent environment. By deliberate design, the proposed Bayesian inverse planning enables each independent agent to infer goals and predict future actions of other agents only by simply observing the past actions of them. To make a timely response to the dynamic environment, the inferences and predictions can be flexibly adjusted accordingly with the continuously updated action information of agents. Based on future action predictions, agents take coordinated actions to achieve Nash equilibrium coordination. We designed several mixed driving scenarios in the real world to test our model, and simulation results show that our model is efficient and flexible, compared with an optimal model and a heuristic model.