Synthesis of Communication Policies for Multi-Agent Systems Robust to Communication Restrictions

TL;DR

This paper presents a method to synthesize joint action and communication policies for multi-agent systems that maximize task success probability while respecting communication bandwidth constraints, demonstrated through experimental benchmarks.

Contribution

It introduces a novel approach to compute robust joint policies that balance task performance with communication restrictions in multi-agent systems.

Findings

Successfully synthesizes policies that respect communication limits.

Achieves high reach-avoid probabilities under constraints.

Demonstrates effectiveness on various benchmark scenarios.

Abstract

We study stochastic multi-agent systems in which agents must cooperate to maximize the probability of achieving a common reach-avoid objective. In many applications, during the execution of the system, the communication between the agents can be constrained by restrictions on the bandwidth currently available for exchanging local-state information between the agents. In this paper, we propose a method for computing joint action and communication policies for the group of agents that aim to satisfy the communication restrictions as much as possible while achieving the optimal reach-avoid probability when communication is unconstrained. Our method synthesizes a pair of action and communication policies robust to restrictions on the number of agents allowed to communicate. To this end, we introduce a novel cost function that measures the amount of information exchanged beyond what the communication policy allows. We evaluate our approach experimentally on a range of benchmarks and demonstrate that it is capable of computing pairs of action and communication policies that satisfy the communication restrictions, if such exist.