POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning
- Harvard SEAS（哈佛大学哈佛约翰·A·保尔森工程与应用科学学院）
- Tufts University（塔夫茨大学）
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英文摘要：
Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs). However, prevailing two-stage approaches that first learn a POMDP and then solve it often fail because the model that best fits the data may not be well suited for planning. We introduce a new optimization objective that (a) produces both high-performing policies and high-quality generative models, even when some observations are irrelevant for planning, and (b) does so in batch off-policy settings that are typical in healthcare, when only retrospective data is available. We demonstrate our approach on synthetic examples and a challenging medical decision-making problem.
