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POMDPs capture a broad class of decision making problems, but hardnessresults suggest that learning is intractable even in simple settings due to theinherent partial observability. However, in many realistic problems, moreinformation is either revealed or can be computed during some point of thelearning process. Motivated by diverse applications ranging from robotics todata center scheduling, we formulate a Hindsight Observable Markov DecisionProcess (HOMDP) as a POMDP where the latent states are revealed to the learnerin hindsight and only during training. We introduce new algorithms for thetabular and function approximation settings that are provably sample-efficientwith hindsight observability, even in POMDPs that would otherwise bestatistically intractable. We give a lower bound showing that the tabularalgorithm is optimal in its dependence on latent state and observationcardinalities.

Learning in POMDPs is Sample-Efficient with Hindsight Observability