partial-observation

HyPOLE introduces a framework for multi-agent reinforcement learning under partial observability that uses hyperproperty-guided learning via HyperLTL temporal logic, integrated with centralized training for decentralized execution, and demonstrates improvements over baselines on SMAC, MessySMAC, and WildFire benchmarks.

Ego2World converts egocentric cooking videos (HD-EPIC) into executable symbolic worlds with graph-transition rules, enabling evaluation of belief-state planning under partial observation. Experiments show that belief memory improves task completion, suggesting it should be a first-class target in embodied agent evaluation.