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While reinforcement learning (RL) has made great advances in scalability,exploration and partial observability are still active research topics. Incontrast, Bayesian RL (BRL) provides a principled answer to both stateestimation and the exploration-exploitation trade-off, but struggles to scale.To tackle this challenge, BRL frameworks with various prior assumptions havebeen proposed, with varied success. This work presents arepresentation-agnostic formulation of BRL under partially observability,unifying the previous models under one theoretical umbrella. To demonstrate itspractical significance we also propose a novel derivation, Bayes-Adaptive DeepDropout rl (BADDr), based on dropout networks. Under this parameterization, incontrast to previous work, the belief over the state and dynamics is a morescalable inference problem. We choose actions through Monte-Carlo tree searchand empirically show that our method is competitive with state-of-the-art BRLmethods on small domains while being able to solve much larger ones.

BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs