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Two of the leading approaches for model-free reinforcement learning are policy gradient methods and $Q$-learning methods.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
A natural policy gradient
Sham Kakade · 2002
Earlier work this paper cites.
Bruno Scherrer · 2010
Earlier work this paper cites.
Modeling purposeful adaptive behavior with the principle of maximum causal entropy
Brian D Ziebart · 2010
Earlier work this paper cites.
Taming the noise in reinforcement learning via soft updates
Roy Fox, Ari Pakman, and Naftali Tishby · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Dueling network architectures for deep reinforcement learning
Ziyu Wang, Nando de Freitas, and Marc Lanctot · 2015
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Gradient estimation using stochastic computation graphs
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel
Cited in the paper.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel
Cited in the paper.
Pgq: Combining policy gradient and q-learning
Brendan O’Donoghue, Remi Munos, Koray Kavukcuoglu, and Volodymyr Mnih · 2016
Later among the works it cites.
Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
Closest in time.
Bridging the gap between value and policy based reinforcement learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans · 2017
Closest in time.
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