Online Learning of Interaction Dynamics with Dual Model Predictive Control for Multi-Agent Systems Using Gaussian Processes

TL;DR

This paper introduces a dual model predictive control approach using Gaussian processes for multi-agent systems, enabling online learning of interaction dynamics and improved prediction in uncertain environments.

Contribution

It presents a novel dual MPC method that jointly optimizes agent actions and beliefs by considering covariance effects in Gaussian process models.

Findings

Enhanced prediction accuracy over baseline stochastic MPC

Successful online learning of agent interactions

Demonstrated effectiveness in autonomous driving simulations

Abstract

The control of a single agent in complex and uncertain multi-agent environments requires careful consideration of the interactions between the agents. In this context, this paper proposes a dual model predictive control (MPC) method using Gaussian process (GP) models for multi-agent systems. While Gaussian process MPC (GP-MPC) has been shown to be effective in predicting the dynamics of other agents, current methods do not consider the influence of the control input on the covariance of the predictions, and hence lack the dual control effect. Therefore, we propose a dual MPC that directly optimizes the actions of the ego agent, and the belief of the other agents by jointly optimizing their state trajectories as well as the associated covariance while considering their interactions through a GP. We demonstrate our GP-MPC method in a simulation study on autonomous driving, showing improved prediction quality compared to a baseline stochastic MPC. The results show that GP-MPC can learn the interactions between the agents online, demonstrating the potential of GPs for dual MPC in uncertain and unseen scenarios.