Towards safe control parameter tuning in distributed multi-agent systems

TL;DR

This paper introduces a safe Bayesian optimization approach with a novel spatio-temporal kernel for tuning control parameters in distributed multi-agent systems, ensuring safety and efficiency in unknown, nonconvex environments.

Contribution

It develops a decentralized safe optimization method using Gaussian processes with a custom spatio-temporal kernel, addressing safety and sample efficiency in multi-agent systems.

Findings

Successful simulation deployment demonstrating effectiveness

Enhanced sample efficiency over prior methods

Effective handling of nonconvex, unknown rewards and constraints

Abstract

Many safety-critical real-world problems, such as autonomous driving and collaborative robots, are of a distributed multi-agent nature. To optimize the performance of these systems while ensuring safety, we can cast them as distributed optimization problems, where each agent aims to optimize their parameters to maximize a coupled reward function subject to coupled constraints. Prior work either studies a centralized setting, does not consider safety, or struggles with sample efficiency. Since we require sample efficiency and work with unknown and nonconvex rewards and constraints, we solve this optimization problem using safe Bayesian optimization with Gaussian process regression. Moreover, we consider nearest-neighbor communication between the agents. To capture the behavior of non-neighboring agents, we reformulate the static global optimization problem as a time-varying local optimization problem for each agent, essentially introducing time as a latent variable. To this end, we propose a custom spatio-temporal kernel to integrate prior knowledge. We show the successful deployment of our algorithm in simulations.