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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.
Provably efficient RL with rich observations via latent state decoding
Simon S Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, and John Langford · 1901
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Reinforcement leaning in feature space: Matrix bandit, kernels, and regret bound
Lin F Yang and Mengdi Wang · 1905
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Is a good representation sufficient for sample efficient reinforcement learning?
Simon S Du, Sham M Kakade, Ruosong Wang, and Lin F Yang · 1910
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Frequentist regret bounds for randomized least-squares value iteration
Andrea Zanette, David Brandfonbrener, Matteo Pirotta, and Alessandro Lazaric · 1911
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An upper bound on the loss from approximate optimal-value functions
Satinder P Singh and Richard C Yee · 1994
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Sham M Kakade · 2003
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Medical qos provision based on reinforcement learning in ultrasound streaming over 3.5 g wireless systems
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Mohammad Gheshlaghi Azar, Rémi Munos, and Hilbert J Kappen · 2013
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Nicolo Cesa-Bianchi, Ofer Dekel, and Ohad Shamir · 2013
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Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2016
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