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The low rank MDP has emerged as an important model for studying representation learning and exploration in reinforcement learning.
Rates of convergence in the central limit theorem for empirical processes
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Provably efficient reinforcement learning with general value function approximation
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Fitted q-iteration in continuous action-space mdps
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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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Algorithms for reinforcement learning
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Introduction to the non-asymptotic analysis of random matrices
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A probabilistic theory of pattern recognition , volume 31
Luc Devroye, László Györfi, and Gábor Lugosi · 2013
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Model-based reinforcement learning and the eluder dimension
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Human-level control through deep reinforcement learning
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Value-aware loss function for model-based reinforcement learning
Amir-massoud Farahmand, Andre Barreto, and Daniel Nikovski · 2017
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Contextual decision processes with low Bellman rank are PAC-learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Sbeed: Convergent reinforcement learning with nonlinear function approximation
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Kinematic state abstraction and provably efficient rich-observation reinforcement learning
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Sample complexity of reinforcement learning using linearly combined model ensembles
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Regret bound balancing and elimination for model selection in bandits and rl
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Learning latent dynamics for planning from pixels
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Batch policy learning under constraints
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Comments on the Du-Kakade-Wang-Yang lower bounds
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Model-based reinforcement learning with value-targeted regression
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Learning invariant representations for reinforcement learning without reconstruction
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Bilinear classes: A structural framework for provable generalization in rl
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