VA-learning as a more efficient alternative to Q-learning

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Q-learning

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Advantage updating

Leemon C. Baird III · 1993

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Tommi Jaakkola, Michael Jordan, and Satinder Singh · 1993

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Tommi Jaakkola, Michael I. Jordan, and Satinder P. Singh · 1994

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John N. Tsitsiklis · 1994

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Residual algorithms: Reinforcement learning with function approximation

Leemon C. Baird · 1995

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Reinforcement learning: An introduction

Richard S. Sutton and Andrew G. Barto · 1998

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Lecture 6.5 - RMSProp

Geoffrey Hinton and Tijman Tieleman · 2012

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The arcade learning environment: An evaluation platform for general agents

Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013

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Playing Atari with deep reinforcement learning

Original

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013

Cited alongside, same era.

Dueling network architectures for deep reinforcement learning

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Van Hasselt, Marc Lanctot, and Nando De Freitas · 2015

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Increasing the action gap: New operators for reinforcement learning

Deep reinforcement learning with double Q-learning

Hado Van Hasselt, Arthur Guez, and David Silver · 2016

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Sample efficient actor-critic with experience replay

Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, and Nando de Freitas · 2016

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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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Gap-increasing policy evaluation for efficient and noise-tolerant reinforcement learning

Original

Tadashi Kozuno, Dongqi Han, and Kenji Doya · 2019

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Deep reinforcement learning at the edge of the statistical precipice

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, and Marc G. Bellemare · 2021

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