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We study distributed Byzantine-resilient actor-critic multi-agent reinforcement learning (AC-MARL), where agents collectively learn policies through local interactions. Existing methods guarantee convergence of the agents'parameters only to a neighborhood of the attack-free limit points, resulting in degraded performan...
Distributed Model-Based Diffusion is proposed, a distributed server-robot framework that decomposes the reverse diffusion process into local conditional reverse diffusion processes that enables each robot to iteratively perform denoising independently within its own control subspace while conditioning on the current tr...
Self-Anchored Consensus (SAC), a fully decentralized filter-and-refine protocol in which agents iteratively exchange responses, locally evaluate and filter unreliable messages, and refine their own outputs, is proposed.
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