zhaoenmin/rl-exploration

Reinforcement Learning papers on exploration methods.

rl-exploration

Reinforcement Learning papers on exploration methods.

Count-based

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

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]

Misc.

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]