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In real-world reinforcement learning problems, the state information is oftenonly partially observable, which breaks the basic assumption in Markov decisionprocesses, and thus, leads to inferior performances. Partially ObservableMarkov Decision Processes have been introduced to explicitly take the issueinto account for learning, exploration, and planning, but presentingsignificant computational and statistical challenges. To address thesedifficulties, we exploit the representation view, which leads to a coherentdesign framework for a practically tractable reinforcement learning algorithmupon partial observations. We provide a theoretical analysis for justifying thestatistical efficiency of the proposed algorithm. We also empiricallydemonstrate the proposed algorithm can surpass state-of-the-art performancewith partial observations across various benchmarks, therefore, pushingreliable reinforcement learning towards more practical applications.

Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning