When Shall I Estimate Your Intent? Costs and Benefits of Intent Inference in Multi-Agent Interactions
Sunny Amatya, Mukesh Ghimire, Yi Ren, Zhe Xu, Wenlong Zhang · 2022 American Control Conference (ACC) · 2022
This paper addresses incomplete-information dynamic games, where reward parameters of agents are private. Previous studies have shown that online belief update is necessary for deriving equilibrial policies of such games, especially for high-risk games such as vehicle interactions. However, updating beliefs in real time is computationally expensive as it requires continuous computation of Nash equilibria of the sub-games starting from the current states. In this paper, we consider the triggering mechanism of belief update as a policy defined on the agents’ physical and belief states, and propose learning this policy through reinforcement learning (RL). Using a two-vehicle uncontrolled intersection case, we show that intermittent belief update via RL is sufficient for safe interactions, reducing the computation cost of updates by 59% when agents have full observations of physical states. Simulation results also show that the belief update frequency will increase as noise becomes more significant in measurements of the vehicle positions.