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.
The impact of agent definitions and interactions on multiagent learning for coordination in traffic management domains,
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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.