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The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.
Neuronlike adaptive elements that can solve difficult learning control problems
A. G. Barto, R. S. Sutton, and C. W. Anderson · 1983
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Evolving virtual creatures
Karl Sims · 1994
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Dynamic programming and optimal control , volume 1
Dimitri P Bertsekas · 1995
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The swing up control problem for the acrobot
Mark W Spong · 1995
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CMU Motion Capture Database
CMU Graphics Lab · 2002
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Reinforcement learning using neural networks, with applications to motor control
Rémi Coulom · 2002
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A unifying computational framework for motor control and social interaction
Daniel Wolpert, Kenji Doya, and Mitsuo Kawato · 2002
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Real-time reinforcement learning by sequential actor–critics and experience replay
Paweł Wawrzyński · 2009
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Stochastic complementarity for local control of discontinuous dynamics
Yuval Tassa and Emo Todorov · 2010
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2012
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Synthesis and stabilization of complex behaviors through online trajectory optimization
Yuval Tassa, Tom Erez, and Emanuel Todorov · 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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Simulation tools for model-based robotics: Comparison of bullet, havok, mujoco, ode and physx
Tom Erez, Yuval Tassa, and Emanuel Todorov · 2015
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Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, and Yuval Tassa · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr et al. Mnih · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Cited alongside, same era.
Learning human behaviors from motion capture by adversarial imitation
Josh Merel, Yuval Tassa, TB Dhruva, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Heess · 2017
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Maximum a posteriori policy optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rémi Munos, Nicolas Heess, and Martin A. Riedmiller · 2018
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Surreal: Open-source reinforcement learning framework and robot manipulation benchmark
Linxi Fan, Yuke Zhu, Jiren Zhu, Zihua Liu, Orien Zeng, Anchit Gupta, Joan Creus-Costa, Silvio Savarese, and Li Fei-Fei · 2018
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, and Martin Riedmiller · 2018
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Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, and Stig Petersen · 2016
Cited alongside, same era.
Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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.
Kinova modular robot arms for service robotics applications
Alexandre Campeau-Lecours, Hugo Lamontagne, Simon Latour, Philippe Fauteux, Véronique Maheu, François Boucher, Charles Deguire, and Louis-Joseph Caron L’Ecuyer · 2017
Cited alongside, same era.
Emergence of locomotion behaviours in rich environments, 2017
Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, and David Silver · 2017
Cited alongside, same era.
Deep reinforcement learning that matters, 2017
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2017
Cited alongside, same era.
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The body is not a given: Joint agent policy learning and morphology evolution
Dylan Banarse, Yoram Bachrach, Siqi Liu, Guy Lever, Nicolas Heess, Chrisantha Fernando, Pushmeet Kohli, and Thore Graepel · 2019
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RLBench: The Robot Learning Benchmark & Learning Environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J. Davison · 2019
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IKEA furniture assembly environment for long-horizon complex manipulation tasks
Youngwoon Lee, Edward S Hu, Zhengyu Yang, Alex Yin, and Joseph J Lim · 2019
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Emergent coordination through competition
Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, and Thore Graepel · 2019
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Reinforcement learning agents acquire flocking and symbiotic behaviour in simulated ecosystems
Peter Sunehag, Guy Lever, Siqi Liu, Josh Merel, Nicolas Heess, Joel Z. Leibo, Edward Hughes, Tom Eccles, and Thore Graepel · 2019
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Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2019
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Deep neuroethology of a virtual rodent
Josh Merel, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, and Bence Ölveczky · 2020
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