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The $Q$-function is a central quantity in many Reinforcement Learning (RL) algorithms for which RL agents behave following a (soft)-greedy policy w.r.t.
Q-learning
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Acme: A research framework for distributed reinforcement learning
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Adam: A method for stochastic optimization
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Human-level control through deep reinforcement learning
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Danilo Rezende and Shakir Mohamed · 2015
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Approximate modified policy iteration and its application to the game of Tetris
Bruno Scherrer, Mohammad Ghavamzadeh, Victor Gabillon, Boris Lesner, and Matthieu Geist · 2015
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Marc G Bellemare, Georg Ostrovski, Arthur Guez, Philip S Thomas, and Rémi Munos · 2016
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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 deep q-learning with model-based acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
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Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas · 2016
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Sbeed: Convergent reinforcement learning with nonlinear function approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, and Le Song · 2018
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Quinoa: a q-function you infer normalized over actions
Jonas Degrave, Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, and Martin Riedmiller · 2018
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
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Trust-pcl: An off-policy trust region method for continuous control
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans · 2018
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Action branching architectures for deep reinforcement learning
Arash Tavakoli, Fabio Pardo, and Petar Kormushev · 2018
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Is the bellman residual a bad proxy?
Matthieu Geist, Bilal Piot, and Olivier Pietquin · 2017
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Luke Metz, Julian Ibarz, Navdeep Jaitly, and James Davidson · 2017
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Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans · 2017
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Maximum a posteriori policy optimisation
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine
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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al
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A Theory of Regularized Markov Decision Processes
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Improving exploration in soft-actor-critic with normalizing flows policies
Patrick Nadeem Ward, Ariella Smofsky, and Avishek Joey Bose · 2019
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Discretizing continuous action space for on-policy optimization
Yunhao Tang and Shipra Agrawal · 2020
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