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We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Policy gradient methods for reinforcement learning with function approximation
Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour · 1999
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A natural policy gradient
Sham M. Kakade · 2001
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Approximately optimal approximate reinforcement learning
Sham M. Kakade and John Langford · 2002
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Smooth optimization with approximate gradient
Alexandre d’Aspremont · 2008
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Relative entropy policy search
Jan Peters, Katharina Mülling, and Yasemin Altun · 2010
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On the computational efficiency of training neural networks
Roi Livni, Shai Shalev-Shwartz, and Ohad Shamir · 2014
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Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2017
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Riashat Islam, Peter Henderson, Maziar Gomrokchi, and Doina Precup · 2017
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Where did my optimum go?: An empirical analysis of gradient descent optimization in policy gradient methods, 2018
Peter Henderson, Joshua Romoff, and Joelle Pineau · 2018
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Simple random search provides a competitive approach to reinforcement learning
Horia Mania, Aurelia Guy, and Benjamin Recht · 2018
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An empirical model of large-batch training, 2018
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OpenAI · 2018
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Towards generalization and simplicity in continuous control
Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, and Sham M. Kakade · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Mastering the game of go without human knowledge
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Understanding the impact of entropy on policy optimization, 2018
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, and Dale Schuurmans · 2018
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, and Pieter Abbeel
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Itay Safran and Ohad Shamir · 2018
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Keynote talk
Ilya Sutskever · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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