On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks
Hairi, Minghong Fang, Zifan Zhang, Alvaro Velasquez, Jia Liu
TL;DR
The paper studies decentralized multi-agent policy evaluation under Byzantine model-poisoning attacks, proving that exact evaluation with a uniform average reward is impossible and that even weighted-average formulations face fundamental limits. It introduces a Byzantine-tolerant decentralized TD algorithm (BDTD) for linear scalar features that guarantees consensus among normal agents, even with heterogeneous rewards and adversarial neighbors. The authors validate robustness experimentally on a 10-agent cooperative navigation task against multiple poisoning attacks, showing BDTD outperforming existing Byzantine-robust baselines. The work advances understanding of consensus under faults in decentralized MARL and provides a practical fault-tolerant algorithm with theoretical guarantees on convergence to a fixed-point determined by the normal agents' rewards.
Abstract
In this paper, we study a fully-decentralized multi-agent policy evaluation problem, which is an important sub-problem in cooperative multi-agent reinforcement learning, in the presence of up to $f$ faulty agents. In particular, we focus on the so-called Byzantine faulty model with model poisoning setting. In general, policy evaluation is to evaluate the value function of any given policy. In cooperative multi-agent system, the system-wide rewards are usually modeled as the uniform average of rewards from all agents. We investigate the multi-agent policy evaluation problem in the presence of Byzantine agents, particularly in the setting of heterogeneous local rewards. Ideally, the goal of the agents is to evaluate the accumulated system-wide rewards, which are uniform average of rewards of the normal agents for a given policy. It means that all agents agree upon common values (the consensus part) and furthermore, the consensus values are the value functions (the convergence part). However, we prove that this goal is not achievable. Instead, we consider a relaxed version of the problem, where the goal of the agents is to evaluate accumulated system-wide reward, which is an appropriately weighted average reward of the normal agents. We further prove that there is no correct algorithm that can guarantee that the total number of positive weights exceeds $|\mathcal{N}|-f $, where $|\mathcal{N}|$ is the number of normal agents. Towards the end, we propose a Byzantine-tolerant decentralized temporal difference algorithm that can guarantee asymptotic consensus under scalar function approximation. We then empirically test the effective of the proposed algorithm.