A redundancy-based filtering Q-learning algorithm makes all agents converge almost surely to optimal values under Byzantine attacks by enforcing a new verifiable topological condition on the communication graph.
Resilient asymptotic consensus in robust networks
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Under a tensor generalized detailed-balance condition, tensor-coupled flow-conservation systems on hypergraphs have a unique equilibrium with global asymptotic stability via an entropy Lyapunov function, plus sensitivity bounds and local ISS linking spectral gap to robustness.
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Fully Byzantine-Resilient Distributed Multi-Agent Q-Learning
A redundancy-based filtering Q-learning algorithm makes all agents converge almost surely to optimal values under Byzantine attacks by enforcing a new verifiable topological condition on the communication graph.
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Stability and Robustness of Tensor-Coupled Flow-Conservation Dynamical Systems on Hypergraphs
Under a tensor generalized detailed-balance condition, tensor-coupled flow-conservation systems on hypergraphs have a unique equilibrium with global asymptotic stability via an entropy Lyapunov function, plus sensitivity bounds and local ISS linking spectral gap to robustness.