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The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Relative entropy policy search
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Terrain-adaptive bipedal locomotion control
Jia-chi Wu and Zoran Popović · 2010
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Robust physics-based locomotion using low-dimensional planning
Igor Mordatch, Martin De Lasa, and Aaron Hertzmann · 2010
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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
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Interactive character animation using simulated physics: A state-of-the-art review
Thomas Geijtenbeek and Nicolas Pronost · 2012
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Terrain runner: control, parameterization, composition, and planning for highly dynamic motions
Libin Liu, KangKang Yin, Michiel van de Panne, and Baining Guo · 2012
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Discovery of complex behaviors through contact-invariant optimization
Igor Mordatch, Emanuel Todorov, and Zoran Popovic · 2012
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Curriculum learning for motor skills
Andrej Karpathy and Michiel Van De Panne · 2012
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Learning neural network policies with guided policy search under unknown dynamics
S. Levine and P. Abbeel · 2014
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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, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Massively parallel methods for deep reinforcement learning
Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, et al · 2015
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End-to-end training of deep visuomotor policies
S Levine, C Finn, T Darrell, and P Abbeel · 2015
Deep reinforcement learning through policy optimization
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Sample efficient actor-critic with experience replay
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, and Nando de Freitas · 2016
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2016
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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https://github.com/joschu/modular_rl , 2016
PPO · 2016
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Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy P. Lillicrap, Tom Erez, and Yuval Tassa · 2015
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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
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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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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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael I Jordan, and Philipp Moritz
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Learning and transfer of modulated locomotor controllers
Nicolas Heess, Greg Wayne, Yuval Tassa, Timothy Lillicrap, Martin Riedmiller, and David Silver · 2016
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Terrain-adaptive locomotion skills using deep reinforcement learning
Xue Bin Peng, Glen Berseth, and Michiel van de Panne · 2016
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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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Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning
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