Reinforcement Learning papers on exploration methods.
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Reinforcement Learning papers on exploration methods.


Count-based exploration is a category of exploration methods that encourage the agent to explore novel states by memorizing states' visitation counts.

Title AKA Author Date Links
Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress - Lopes et al. 2012.12 [Paper]
An Analytic Solution to Discrete Bayesian Reinforcement Learning BEETLE Poupart et al. 2006.06 [Paper]
Unifying Count-Based Exploration and Intrinsic Motivation DQN-CTS Bellmare et al. 2016.06 [Paper]
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning - Tang et al. 2016.11 [Paper]
Count-Based Exploration with Neural Density Models DQN-PixelCNN Ostrovski et al. 2017.03 [Paper]


Curiosity-driven exploration is a category of exploration methods that "generate an intrinsic reward signal based on how hard it is for the agent to predict the consequences of its own actions, i.e. predict the next state given the current state and the executed action." (Pathak et al., 2017)

Title AKA Author Date Links
Curious Model-building Control Systems - Schmidhuber 1991.11 [Paper]
Reinforcement-Driven Information Acquisition in Non-deterministic Environments - Storck et al. 1995 [Paper]
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) - Schmidhuber 2010.07 [Paper]
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments - Sun et al. 2011.03 [Paper]
VIME: Variational Information Maximizing Exploration VIME Houthooft et al. 2016.05 [Paper]
Curiosity-driven Exploration by Self-supervised Prediction ICM Pathak et al. 2017.05 [Paper]
Large-Scale Study of Curiosity-Driven Learning ICM Burda et al. 2018.08 [Paper]


Title AKA Author Date Links
Intrinsically Motivated Reinforcement Learning - Singh et al. 2004 [Paper]
A Theoretical Analysis of Model-Based Interval Estimation MBIE Strehl and Littman 2005.08 [Paper]
An Analysis of Model-based Interval Estimation for Markov Decision Processes MBIE-EB Strehl and Littman 2008.09 [Paper]
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models - Stadie et al. 2015.07 [Paper]
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning - Mohamed and Rezende 2015.09 [Paper]