A fault-tolerant MARL method using attention in actor and critic networks plus per-module prioritized experience replay improves team performance when agents suddenly fail.
A survey on multi- agent reinforcement learning applications in the internet of vehicles,
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Towards Fault Tolerance in Multi-Agent Reinforcement Learning
A fault-tolerant MARL method using attention in actor and critic networks plus per-module prioritized experience replay improves team performance when agents suddenly fail.