A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost
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Many real-world applications, such as those in medical domains, recommendation systems, etc, can be formulated as large state space reinforcement learning problems with only a small budget of the number of policy changes, i.e., low switching cost. This paper focuses on the linear Markov Decision Process (MDP) recently studied in [Yang et al 2019, Jin et al 2020] where the linear function approximation is used for generalization on the large state space. We present the first algorithm for linear MDP with a low switching cost. Our algorithm achieves an O(√(d^3H^4K)) regret bound with a near-optimal O(d Hlog K) global switching cost where d is the feature dimension, H is the planning horizon and K is the number of episodes the agent plays. Our regret bound matches the best existing polynomial algorithm by [Jin et al 2020] and our switching cost is exponentially smaller than theirs. When specialized to tabular MDP, our switching cost bound improves those in [Bai et al 2019, Zhang et al 20020]. We complement our positive result with an Ω(dH/log d) global switching cost lower bound for any no-regret algorithm.
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