Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL

• URL: http://arxiv.org/abs/2008.13319v3
• Date: Wed, 10 Mar 2021 01:58:03 GMT
• Title: Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL
• Authors: Xiaoyu Chen, Jiachen Hu, Lihong Li, Liwei Wang
• Abstract summary: Reinforcement learning in episodic, factored Markov decision processes (FMDPs) is studied. We propose an algorithm called FMDP-BF, which leverages the factorization structure of FMDP. As an application, we study a new formulation of constrained RL, known as RL with knapsack constraints (RLwK)
• Score: 25.119552984253882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) in episodic, factored Markov decision processes (FMDPs) is studied. We propose an algorithm called FMDP-BF, which leverages the factorization structure of FMDP. The regret of FMDP-BF is shown to be exponentially smaller than that of optimal algorithms designed for non-factored MDPs, and improves on the best previous result for FMDPs~\citep{osband2014near} by a factored of $\sqrt{H|\mathcal{S}_i|}$, where $|\mathcal{S}_i|$ is the cardinality of the factored state subspace and $H$ is the planning horizon. To show the optimality of our bounds, we also provide a lower bound for FMDP, which indicates that our algorithm is near-optimal w.r.t. timestep $T$, horizon $H$ and factored state-action subspace cardinality. Finally, as an application, we study a new formulation of constrained RL, known as RL with knapsack constraints (RLwK), and provides the first sample-efficient algorithm based on FMDP-BF.

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