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We focus on the task of approximating the optimal value function in deep reinforcement learning.
Some methods of speeding up the convergence of iteration methods
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Human-level control through deep reinforcement learning
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Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas · 2016
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Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
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Kavosh Asadi, Neev Parikh, Ronald E Parr, George D Konidaris, and Michael L Littman · 2021
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The value-improvement path: Towards better representations for reinforcement learning
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G Bellemare, and David Silver · 2021
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Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, and Michael L Littman · 2021
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The nature of temporal difference errors in multi-step distributional reinforcement learning
Yunhao Tang, Remi Munos, Mark Rowland, Bernardo Avila Pires, Will Dabney, and Marc Bellemare · 2022
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Multi-step reinforcement learning: A unifying algorithm
Kristopher De Asis, J Hernandez-Garcia, G Holland, and Richard Sutton · 2018
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