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Teaching robots to learn diverse locomotion skills under complex three-dimensional environmental settings via Reinforcement Learning (RL) is still challenging.
Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)
Nikolaus Hansen, Sibylle Müller, and Petros Koumoutsakos · 2003
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A simple reinforcement learning algorithm for biped walking
J. Morimoto, G. Cheng, C.G. Atkeson, and G. Zeglin · 2004
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Compositional pattern producing networks: A novel abstraction of development
Kenneth Stanley · 2007
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Jan Peters, and Jürgen Schmidhuber · 2008
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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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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Learning complex neural network policies with trajectory optimization
Sergey Levine and Vladlen Koltun · 2014
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber · 2014
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Learning Continuous Control Policies by Stochastic Value Gradients
Nicolas Heess, Greg Wayne, David Silver, Timothy P. Lillicrap, Yuval Tassa, and Tom Erez · 2015
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Terrain-adaptive locomotion skills using deep reinforcement learning
Xue Bin Peng, Glen Berseth, and Michiel van de Panne · 2016
Cited alongside, same era.
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2017
Cited alongside, same era.
Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
Cited alongside, same era.
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
Learning generalizable locomotion skills with hierarchical reinforcement learning
Tianyu Li, Nathan O. Lambert, Roberto Calandra, Franziska Meier, and Akshara Rai · 2019
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Exploring dynamic locomotion of a quadruped robot: a study of reinforcement learning for the anymal robot
Branislav Pilňan · 2019
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Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O. Stanley · 2019
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Blind Hexapod Locomotion in Complex Terrain with Gait Adaptation Using Deep Reinforcement Learning and Classification
Teymur Azayev and Karel Zimmerman · 2020
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CARL: controllable agent with reinforcement learning for quadruped locomotion
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Evolution strategies as a scalable alternative to reinforcement learning, 2017
Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever · 2017
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Physics-based motion capture imitation with deep reinforcement learning
Nuttapong Chentanez, Matthias Müller, Miles Macklin, Viktor Makoviychuk, and Stefan Jeschke · 2018
Cited alongside, same era.
Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
The hybercube-based neuroevolution of augmenting topologies: http://eplex.cs.ucf.edu/hyperneatpage/hyperneat.html
Kenneth O. Stanley
Cited in the paper.
The neuroevolution of augmenting topologies: https://www.cs.ucf.edu/ kstanley/neat.html
Kenneth O. Stanley
Cited in the paper.
Ying-Sheng Luo, Jonathan Hans Soeseno, Trista Pei-Chun Chen, and Wei-Chao Chen · 2020
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Evolve To Control: Evolution-based Soft Actor-Critic for Scalable Reinforcement Learning
Karush Suri, Xiao Qi Shi, Konstantinos N. Plataniotis, and Yuri A. Lawryshyn · 2020
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Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions, 2020
Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeff Clune, and Kenneth O. Stanley · 2020
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Fiber: A platform for efficient development and distributed training for reinforcement learning and population-based methods, 2020
Jiale Zhi, Rui Wang, Jeff Clune, and Kenneth O. Stanley · 2020
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Pytorch implementation of reinforcement learning methods : https://github.com/rchalyang/torchrl, 2021
Rchal Yang · 2021
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