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<p>In a cooperative multiagent system, a collection of agents executes a joint policy in order to achieve some common objective. The successful deployment of such systems hinges on the availability of reliable inter-agent communication. However, many sources of potential disruption to communication exist in practice, such as radio interference, hardware failure, and adversarial attacks. In this work, we develop joint policies for cooperative multiagent systems that are robust to potential losses in communication. More specifically, we develop joint policies for cooperative Markov games with independent transitions and joint reach-avoid objectives. First, we propose an algorithm for the decentralized execution of joint policies during periods of communication loss. This algorithm is designed to work under arbitrary communication partitions between the agents. Next, we use the total correlation of the state-action process induced by a joint policy as a measure of the intrinsic dependencies between the agents. We then use this measure to lower-bound the performance of a joint policy under randomly intermittent or adversarial communication loss scenarios. We show the existence of a multiagent decision-making environment in which this bound is tight—the highest performance under intermittent communication loss, for any policy execution mechanism, is of the same order as the bound. We then present an algorithm that maximizes a proxy to this lower bound in order to synthesize minimum-dependency joint policies that remain performant under communication loss. Through two-agent and three-agent numerical experiments, we show that the proposed minimum-dependency policies require minimal coordination between the agents while incurring little to no loss in performance; the total correlation value of the synthesized policy is significantly lower than the total correlation value of the baseline policy which does not take potential communication losses into account. As a result, the performance of the minimum-dependency policies remains consistently high regardless of whether or not communication is available. By contrast, the performance of the baseline policy decreases drastically when communication is lost.</p>

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Designing policies for transition-independent multiagent systems that are robust to communication loss

  • 期刊论文

  • 发表日期：

    2025年8月21日

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  • Mustafa O. Karabag,
  • Cyrus Neary,
  • Ufuk Topcu

摘要

In a cooperative multiagent system, a collection of agents executes a joint policy in order to achieve some common objective. The successful deployment of such systems hinges on the availability of reliable inter-agent communication. However, many sources of potential disruption to communication exist in practice, such as radio interference, hardware failure, and adversarial attacks. In this work, we develop joint policies for cooperative multiagent systems that are robust to potential losses in communication. More specifically, we develop joint policies for cooperative Markov games with independent transitions and joint reach-avoid objectives. First, we propose an algorithm for the decentralized execution of joint policies during periods of communication loss. This algorithm is designed to work under arbitrary communication partitions between the agents. Next, we use the total correlation of the state-action process induced by a joint policy as a measure of the intrinsic dependencies between the agents. We then use this measure to lower-bound the performance of a joint policy under randomly intermittent or adversarial communication loss scenarios. We show the existence of a multiagent decision-making environment in which this bound is tight—the highest performance under intermittent communication loss, for any policy execution mechanism, is of the same order as the bound. We then present an algorithm that maximizes a proxy to this lower bound in order to synthesize minimum-dependency joint policies that remain performant under communication loss. Through two-agent and three-agent numerical experiments, we show that the proposed minimum-dependency policies require minimal coordination between the agents while incurring little to no loss in performance; the total correlation value of the synthesized policy is significantly lower than the total correlation value of the baseline policy which does not take potential communication losses into account. As a result, the performance of the minimum-dependency policies remains consistently high regardless of whether or not communication is available. By contrast, the performance of the baseline policy decreases drastically when communication is lost.