Directed Exploration in PAC Model-Free Reinforcement Learning

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Singh, Satinder, Jaakkola, Tommi, Littman, Michael L, and Szepesvári, Csaba · 2000

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R-max-a general polynomial time algorithm for near-optimal reinforcement learning

Brafman, Ronen I and Tennenholtz, Moshe · 2002

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Near-optimal reinforcement learning in polynomial time

Kearns, Michael and Singh, Satinder · 2002

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On the sample complexity of reinforcement learning

Kakade, Sham Machandranath · 2003

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A theoretical analysis of model-based interval estimation

Strehl, Alexander L and Littman, Michael L · 2005

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Pac model-free reinforcement learning

Strehl, Alexander L, Li, Lihong, Wiewiora, Eric, Langford, John, and Littman, Michael L · 2006

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Probably approximately correct (PAC) exploration in reinforcement learning

Strehl, Alexander L · 2007

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An analysis of model-based interval estimation for markov decision processes

Strehl, Alexander L and Littman, Michael L · 2008

Cited alongside, same era.

The many faces of optimism: a unifying approach

Szita, István and Lőrincz, András · 2008

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Near-bayesian exploration in polynomial time

Kolter, J Zico and Ng, Andrew Y · 2009

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A unifying framework for computational reinforcement learning theory

Li, Lihong · 2009

Cited alongside, same era.

Reinforcement learning in finite mdps: Pac analysis

Strehl, Alexander L, Li, Lihong, and Littman, Michael L · 2009

Cited alongside, same era.

Efficient exploration in reinforcement learning

Original

Thrun, Sebastian B · 2009

Learning and exploration in action-perception loops

Little, Daniel Ying-Jeh and Sommer, Friedrich Tobias · 2013

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Unifying count-based exploration and intrinsic motivation

Bellemare, Marc, Srinivasan, Sriram, Ostrovski, Georg, Schaul, Tom, Saxton, David, and Munos, Remi · 2016

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Generalization and exploration via randomized value functions

Osband, Ian, Van Roy, Benjamin, and Wen, Zheng · 2016

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Count-based exploration with neural density models

Original

Ostrovski, Georg, Bellemare, Marc G, Oord, Aaron van den, and Munos, Rémi · 2017

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# exploration: A study of count-based exploration for deep reinforcement learning

Tang, Haoran, Houthooft, Rein, Foote, Davis, Stooke, Adam, Chen, OpenAI Xi, Duan, Yan, Schulman, John, DeTurck, Filip, and Abbeel, Pieter · 2017

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