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Many real-world applications of reinforcement learning (RL) require the agent to deal with high-dimensional observations such as those generated from a megapixel camera.
Approximately optimal approximate reinforcement learning
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J Andrew Bagnell, Sham M Kakade, Jeff G Schneider, and Andrew Y Ng · 2004
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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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Unifying PAC and regret: Uniform PAC bounds for episodic reinforcement learning
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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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Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A Efros · 2018
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Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2018
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Discovering and removing exogenous state variables and rewards for reinforcement learning
Thomas G Dietterich, George Trimponias, and Zhitang Chen · 2018
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Online convex optimization in adversarial markov decision processes
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Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
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Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, and John Langford · 2020
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Sample complexity of reinforcement learning using linearly combined model ensembles
Aditya Modi, Nan Jiang, Ambuj Tewari, and Satinder Singh · 2020
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Planning from pixels using inverse dynamics models
Keiran Paster, Sheila A McIlraith, and Jimmy Ba · 2020
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Adaptive trust region policy optimization: Global convergence and faster rates for regularized mdps
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2019
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Provably efficient RL with rich observations via latent state decoding
Simon S Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, and John Langford · 2019
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Deepmdp: Learning continuous latent space models for representation learning
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, and Marc G Bellemare · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Flambe: Structural complexity and representation learning of low rank mdps
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, and Wen Sun
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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
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Lior Shani, Yonathan Efroni, and Shie Mannor · 2020
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Learning invariant representations for reinforcement learning without reconstruction
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Reinforcement learning with trajectory feedback
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Learning invariant representations for reinforcement learning without reconstruction
Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine · 2021
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