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Standard model-free deep reinforcement learning (RL) algorithms sample a new initial state for each trial, allowing them to optimize policies that can perform well even in highly stochastic environments.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Minoru Asada, Shoichi Noda, Sukoya Tawaratsumida, and Koh Hosoda · 1996
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A natural policy gradient
Sham M Kakade · 2002
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Curriculum Learning for Motor Skills , pp. 325–330
Andrej Karpathy and Michiel van de Panne · 2012
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Learning throwing and catching skills
Jens Kober, Katharina Mülling, and Jan Peters · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Guided policy search
Sergey Levine and Vladlen Koltun · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Combining the benefits of function approximation and trajectory optimization
Igor Mordatch and Emanuel Todorov · 2014
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Contextual Markov Decision Processes
Assaf Hallak, Dotan Di Castro, and Shie Mannor · 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.
Interactive Control of Diverse Complex Characters with Neural Networks
Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, and Emanuel Todorov · 2015
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael Jordan, and Pieter Abbeel · 2015
Cited alongside, same era.
Openai gym, 2016
End-to-end learning of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Later among the works it cites.
Experiments with hierarchical reinforcement learning of multiple grasping policies
Takayuki Osa, Jan Peters, and Gerhard Neumann · 2016
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Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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Emergence of locomotion behaviours in rich environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin A. Riedmiller, and David Silver · 2017
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Overcoming exploration in reinforcement learning with demonstrations
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Optimal control with learned local models: Application to dexterous manipulation
Vikash Kumar, Emanuel Todorov, and Sergey Levine · 2016
Cited alongside, same era.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, and Sergey Levine
Cited in the paper.
Towards Generalization and Simplicity in Continuous Control
Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, and Sham Kakade
Cited in the paper.
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2017
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Data-efficient deep reinforcement learning for dexterous manipulation
Ivaylo Popov, Nicolas Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerik, Thomas Lampe, Yuval Tassa, Tom Erez, and Martin A. Riedmiller · 2017
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Distral: Robust multitask reinforcement learning
Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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