AI RESEARCH
Guided Policy Optimization under Partial Observability
arXiv CS.AI
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ArXi:2505.15418v2 Announce Type: replace-cross Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty. While additional information, such as that available in simulations, can enhance