Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting

• URL: http://arxiv.org/abs/2002.02302v2
• Date: Sat, 6 Jun 2020 03:30:47 GMT
• Title: Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting
• Authors: Ziping Xu and Ambuj Tewari
• Abstract summary: We study reinforcement learning in non-episodic factored Markov decision processes (FMDPs) We propose two near-optimal and oracle-efficient algorithms for FMDPs. Our oracle-efficient algorithms outperform previously proposed near-optimal algorithms on computer network administration simulations.
• Score: 24.90164851620799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study reinforcement learning in non-episodic factored Markov decision processes (FMDPs). We propose two near-optimal and oracle-efficient algorithms for FMDPs. Assuming oracle access to an FMDP planner, they enjoy a Bayesian and a frequentist regret bound respectively, both of which reduce to the near-optimal bound $\widetilde{O}(DS\sqrt{AT})$ for standard non-factored MDPs. We propose a tighter connectivity measure, factored span, for FMDPs and prove a lower bound that depends on the factored span rather than the diameter $D$. In order to decrease the gap between lower and upper bounds, we propose an adaptation of the REGAL.C algorithm whose regret bound depends on the factored span. Our oracle-efficient algorithms outperform previously proposed near-optimal algorithms on computer network administration simulations.

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