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Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning.
Bidirectional relation between cma evolution strategies and natural evolution strategies
Youhei Akimoto, Yuichi Nagata, Isao Ono, and Shigenobu Kobayashi · 2010
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Exponential natural evolution strategies
Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, and Jürgen Schmidhuber · 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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Robots that can adapt like animals
Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret · 2015
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Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, and Philipp Moritz · 2015
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The CMA evolution strategy: A tutorial
Nikolaus Hansen · 2016
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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 · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, and Pieter Abbeel · 2016
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Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm
Vassilis Vassiliades, Konstantinos Chatzilygeroudis, and Jean-Baptiste Mouret · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth Stanley, and Jeff Clune · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
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Data-Efficient Design Exploration through Surrogate-Assisted Illumination
Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret · 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
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Talakat: Bullet hell generation through constrained map-elites
Ahmed Khalifa, Scott Lee, Andy Nealen, and Julian Togelius · 2018
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Challenges in high-dimensional reinforcement learning with evolution strategies
Nils Müller and Tobias Glasmachers · 2018
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Discovering the elite hypervolume by leveraging interspecies correlation
Vassilis Vassiliades and Jean-Baptiste Mouret · 2018
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Procedural content generation through quality diversity
Isaac gym: High performance GPU based physics simulation for robot learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State · 2021
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Policy gradient assisted map-elites
Olle Nilsson and Antoine Cully · 2021
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Learning to walk in minutes using massively parallel deep reinforcement learning
Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter · 2021
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Illuminating diverse neural cellular automata for level generation
Sam Earle, Justin Snider, Matthew C. Fontaine, Stefanos Nikolaidis, and Julian Togelius · 2022
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Dextreme: Transfer of agile in-hand manipulation from simulation to reality
Ankur Handa, Arthur Allshire, Viktor Makoviychuk, Aleksei Petrenko, Ritvik Singh, Jingzhou Liu, Denys Makoviichuk, Karl Van Wyk, Alexander Zhurkevich, Balakumar Sundaralingam, Yashraj Narang, Jean-Francois Lafleche, Dieter Fox, and Gavriel State · 2022
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Daniele Gravina, Ahmed Khalifa, Antonios Liapis, Julian Togelius, and Georgios N Yannakakis · 2019
Cited alongside, same era.
Scaling map-elites to deep neuroevolution
Cédric Colas, Vashisht Madhavan, Joost Huizinga, and Jeff Clune · 2020
Cited alongside, same era.
Covariance matrix adaptation for the rapid illumination of behavior space
Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis, and Amy K. Hoover · 2020
Cited alongside, same era.
Designing air flow with surrogate-assisted phenotypic niching
Alexander Hagg, Dominik Wilde, Alexander Asteroth, and Thomas Bäck · 2020
Cited alongside, same era.
Egad! an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation
Douglas Morrison, Peter Corke, and Jürgen Leitner · 2020
Cited alongside, same era.
Decentralized control of quadrotor swarms with end-to-end deep reinforcement learning
Sumeet Batra, Zhehui Huang, Aleksei Petrenko, Tushar Kumar, Artem Molchanov, and Gaurav S. Sukhatme · 2021
Cited alongside, same era.
Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization , pp. 109–135
Konstantinos Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades, and Jean-Baptiste Mouret · 2021
Cited alongside, same era.
Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms
Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, and João G.M. Araújo · 2022
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Accelerated quality-diversity for robotics through massive parallelism
Bryan Lim, Maxime Allard, Luca Grillotti, and Antoine Cully · 2022
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Automatic acquisition of a repertoire of diverse grasping trajectories through behavior shaping and novelty search
Aurélien Morel, Yakumo Kunimoto, Alex Coninx, and Stéphane Doncieux · 2022
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Diversity policy gradient for sample efficient quality-diversity optimization
Thomas Pierrot, Valentin Macé, Felix Chalumeau, Arthur Flajolet, Geoffrey Cideron, Karim Beguir, Antoine Cully, Olivier Sigaud, and Nicolas Perrin-Gilbert · 2022
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Empirical analysis of pga-map-elites for neuroevolution in uncertain domains
Manon Flageat, Felix Chalumeau, and Antoine Cully · 2023
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Covariance matrix adaptation map-annealing
Matthew Fontaine and Stefanos Nikolaidis · 2023
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Evolving populations of diverse RL agents with map-elites
Thomas Pierrot and Arthur Flajolet · 2023
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Pyribs: A bare-bones python library for quality diversity optimization
Bryon Tjanaka, Matthew C Fontaine, David H Lee, Yulun Zhang, Nivedit Reddy Balam, Nathaniel Dennler, Sujay S Garlanka, Nikitas Dimitri Klapsis, and Stefanos Nikolaidis · 2023
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