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Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL), with many naive approaches succumbing to exponential sample complexity.
Constrained Markov decision processes , volume 7
Eitan Altman · 1999
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R-max-a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I Brafman and Moshe Tennenholtz · 2002
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Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
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Near-optimal reinforcement learning in polynomial time
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Andrea Zanette and Emma Brunskill · 2002
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Sham Machandranath Kakade · 2003
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Learning near-optimal policies with bellman-residual minimization based fitted policy iteration and a single sample path
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Finite-time bounds for fitted value iteration
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Sample complexity bounds of exploration
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Sample complexity of episodic fixed-horizon reinforcement learning
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Chen Tessler, Daniel J Mankowitz, and Shie Mannor · 2018
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Optimality and approximation with policy gradient methods in markov decision processes
Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan · 2019
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Provably efficient exploration in policy optimization
Qi Cai, Zhuoran Yang, Chi Jin, and Zhaoran Wang · 2019
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Information-theoretic considerations in batch reinforcement learning
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Provably efficient rl with rich observations via latent state decoding
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Reinforcement learning with convex constraints
Sobhan Miryoosefi, Kianté Brantley, Hal Daumé III, Miroslav Dudik, and Robert Schapire · 2019
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Kinematic state abstraction and provably efficient rich-observation reinforcement learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, and John Langford · 2019
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Non-asymptotic gap-dependent regret bounds for tabular mdps
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