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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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.