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Consider the problem of training robustly capable agents.
Natural Gradient Works Efficiently in Learning
Shun-ichi Amari. 1998 · 1998
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Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML ’99) . Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 278–287
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Evolution strategies – A comprehensive introduction
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A Tutorial on the Cross-Entropy Method
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QD-RL: Efficient Mixing of Quality and Diversity in Reinforcement Learning
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Natural Evolution Strategies. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) . 3381–3387
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Bidirectional Relation between CMA Evolution Strategies and Natural Evolution Strategies. In Parallel Problem Solving from Nature, PPSN XI , Robert Schaefer, Carlos Cotta, Joanna Kołodziej, and Günter Rudolph (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 154–163
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Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 9) , Yee Whye Teh and Mike Titterington (Eds.). PMLR, Chia Laguna Resort, Sardinia, Italy, 249–256
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Abandoning Objectives: Evolution Through the Search for Novelty Alone
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Robots that can adapt like animals
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Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune. 2015 · 2015
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Universal Value Function Approximators. In Proceedings of the 32nd International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 37) , Francis Bach and David Blei (Eds.). PMLR, Lille, France, 1312–1320
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Trust Region Policy Optimization. In Proceedings of the 32nd International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 37) , Francis Bach and David Blei (Eds.). PMLR, Lille, France, 1889–1897
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
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Faulty Reward Functions in the Wild
Jack Clark and Dario Amodei. 2016 · 2016
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The CMA Evolution Strategy: A Tutorial
Nikolaus Hansen. 2016 · 2016
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Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2016 · 2016
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Quality Diversity: A New Frontier for Evolutionary Computation
Justin K. Pugh, Lisa B. Soros, and Kenneth O. Stanley. 2016 · 2016
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Hindsight Experience Replay. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
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A Visual Guide to Evolution Strategies
David Ha. 2017 · 2017
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InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
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Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Diversity is All You Need: Learning Skills without a Reward Function. In International Conference on Learning Representations
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. 2019 · 2019
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Procedural content generation through quality diversity. In 2019 IEEE Conference on Games (CoG) . IEEE, 1–8
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Collaborative Evolutionary Reinforcement Learning. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 3341–3350
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Solving Rubik’s Cube with a Robot Hand
OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, and Lei Zhang. 2019 · 2019
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Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever. 2017 · 2017
Cited alongside, same era.
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . 23–30
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. 2017 · 2017
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GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, 1039–1048
Cédric Colas, Olivier Sigaud, and Pierre-Yves Oudeyer. 2018 · 2018
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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 · 2018
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Addressing Function Approximation Error in Actor-Critic Methods. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, 1587–1596
Scott Fujimoto, Herke van Hoof, and David Meger. 2018 · 2018
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Data-efficient design exploration through surrogate-assisted illumination
Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret. 2018 · 2018
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Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, 1861–1870
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. 2018 · 2018
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CEM-RL: Combining evolutionary and gradient-based methods for policy search. In International Conference on Learning Representations
Pourchot and Sigaud. 2019 · 2019
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Scaling MAP-Elites to Deep Neuroevolution. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (Cancún, Mexico) (GECCO ’20) . Association for Computing Machinery, New York, NY, USA, 67–75
Cédric Colas, Vashisht Madhavan, Joost Huizinga, and Jeff Clune. 2020 · 2020
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PyBullet, a Python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai. 2016–2020 · 2020
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Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (Cancún, Mexico) (GECCO ’20) . Association for Computing Machinery, New York, NY, USA, 94–102
Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis, and Amy K. Hoover. 2020 · 2020
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Discovering Representations for Black-Box Optimization. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (Cancún, Mexico) (GECCO ’20) . Association for Computing Machinery, New York, NY, USA, 103–111
Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret. 2020 · 2020
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One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
Saurabh Kumar, Aviral Kumar, Sergey Levine, and Chelsea Finn. 2020 · 2020
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Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization
Paolo Pagliuca, Nicola Milano, and Stefano Nolfi. 2020 · 2020
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Effective Diversity in Population Based Reinforcement Learning. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 18050–18062
Jack Parker-Holder, Aldo Pacchiano, Krzysztof M Choromanski, and Stephen J Roberts. 2020 · 2020
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A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy
Matthew Fontaine and Stefanos Nikolaidis. 2021a · 2021
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On the Importance of Environments in Human-Robot Coordination
Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang, Bryon Tjanaka, and Stefanos Nikolaidis. 2021 · 2021
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Differentiable Quality Diversity
Matthew C. Fontaine and Stefanos Nikolaidis. 2021b · 2021
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Policy Gradient Assisted MAP-Elites. In Proceedings of the Genetic and Evolutionary Computation Conference (Lille, France) (GECCO ’21) . Association for Computing Machinery, New York, NY, USA, 866–875
Olle Nilsson and Antoine Cully. 2021 · 2021
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Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-Based Neuroevolution. In Proceedings of the Genetic and Evolutionary Computation Conference (Lille, France) (GECCO ’21) . Association for Computing Machinery, New York, NY, USA, 901–909
Nemanja Rakicevic, Antoine Cully, and Petar Kormushev. 2021 · 2021
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Guiding Evolutionary Strategies with Off-Policy Actor-Critic. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (Virtual Event, United Kingdom) (AAMAS ’21) . International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 1317–1325
Yunhao Tang. 2021 · 2021
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pyribs: A bare-bones Python library for quality diversity optimization
Bryon Tjanaka, Matthew C. Fontaine, Yulun Zhang, Sam Sommerer, Nathan Dennler, and Stefanos Nikolaidis. 2021 · 2021
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