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Entropy regularization is used to get improved optimization performance in reinforcement learning tasks.
Infobot: Transfer and exploration via the information bottleneck
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Sergey Levine, and Yoshua Bengio · 1901
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Information asymmetry in kl-regularized rl
Alexandre Galashov, Siddhant M Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M Czarnecki, Yee Whye Teh, Razvan Pascanu, and Nicolas Heess · 1905
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Policy gradient methods for reinforcement learning with function approximation
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y. Ng, Daishi Harada, and Stuart J. Russell · 1999
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Actor-critic algorithms
Vijay R Konda and John N Tsitsiklis · 2000
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Universal value function approximators
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High-dimensional continuous control using generalized advantage estimation
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Unifying count-based exploration and intrinsic motivation
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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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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Vfunc: a deep generative model for functions
Philip Bachman, Riashat Islam, Alessandro Sordoni, and Zafarali Ahmed · 2018
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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gym-miniworld environment for openai gym
Maxime Chevalier-Boisvert · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Recall traces: Backtracking models for efficient reinforcement learning
Anirudh Goyal, Philemon Brakel, William Fedus, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, and Yoshua Bengio · 2018
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Count-based exploration with neural density models
Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, and Rémi Munos · 2017
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Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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Equivalence between policy gradients and soft q-learning
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov
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Pytorch implementations of reinforcement learning algorithms
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Addressing function approximation error in actor-critic methods
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Understanding the impact of entropy on policy optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, and Dale Schuurmans · 2019
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Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2019
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Skew-fit: State-covering self-supervised reinforcement learning
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