HyPOLE introduces a HyperLTL-guided framework for partial-observability MARL integrated with CTDE, claiming advantages over baselines on SMAC, MessySMAC, and WildFire.
Proceedings of the International Conference on Automated Planning and Scheduling , author=
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HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation
HyPOLE introduces a HyperLTL-guided framework for partial-observability MARL integrated with CTDE, claiming advantages over baselines on SMAC, MessySMAC, and WildFire.