Oracle-Efficient Reinforcement Learning in Factored MDPs with Unknown Structure
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We consider provably-efficient reinforcement learning (RL) in non-episodic factored Markov decision processes (FMDPs). All previous algorithms for regret minimization in this setting made the strong assumption that the factored structure of the FMDP is known to the learner in advance. In this paper, we provide the first provably-efficient algorithm that has to learn the structure of the FMDP while minimizing its regret. Our algorithm is based on the optimism in face of uncertainty principle, combined with a simple statistical method for structure learning, and can be implemented efficiently given oracle-access to an FMDP planner. It maintains its computational efficiency even though the number of possible structures is exponential.
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