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Deep reinforcement learning primarily focuses on learning behavior, usually overlooking the fact that an agent's function is largely determined by form.
Evolving virtual creatures
Karl Sims · 1994
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The im algorithm: a variational approach to information maximization
David Barber and Felix V Agakov · 2003
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Empowerment: A universal agent-centric measure of control
Alexander S Klyubin, Daniel Polani, and Chrystopher L Nehaniv · 2005
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Evolving a diversity of virtual creatures through novelty search and local competition
Joel Lehman and Kenneth O Stanley · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
Nick Cheney, Robert MacCurdy, Jeff Clune, and Hod Lipson · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Variational information maximisation for intrinsically motivated reinforcement learning
Shakir Mohamed and Danilo Jimenez Rezende · 2015
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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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One-shot learning of manipulation skills with online dynamics adaptation and neural network priors
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 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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Distributed distributional deterministic policy gradients
Gabriel Barth-Maron, Matthew W Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva Tb, Alistair Muldal, Nicolas Heess, and Timothy Lillicrap · 2018
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Scalable co-optimization of morphology and control in embodied machines
Nick Cheney, Josh Bongard, Vytas SunSpiral, and Hod Lipson · 2018
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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
Learning to control self-assembling morphologies: a study of generalization via modularity
Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, and Alexei A Efros · 2019
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Stable baselines3
Antonin Raffin, Ashley Hill, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, and Noah Dormann · 2019
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Jointly learning to construct and control agents using deep reinforcement learning
Charles Schaff, David Yunis, Ayan Chakrabarti, and Matthew R Walter · 2019
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Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2019
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Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Automated design of manipulators for in-hand tasks
Christopher Hazard, Nancy Pollard, and Stelian Coros · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 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, et al · 2018
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Nervenet: Learning structured policy with graph neural networks
Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler · 2018
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Reinforcement learning for improving agent design
David Ha · 2019
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Tingwu Wang, Yuhao Zhou, Sanja Fidler, and Jimmy Ba · 2019
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Hardware as policy: Mechanical and computational co-optimization using deep reinforcement learning
Tianjian Chen, Zhanpeng He, and Matei Ciocarlie · 2020
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One policy to control them all: Shared modular policies for agent-agnostic control
Wenlong Huang, Igor Mordatch, and Deepak Pathak · 2020
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Data-efficient co-adaptation of morphology and behaviour with deep reinforcement learning
Kevin Sebastian Luck, Heni Ben Amor, and Roberto Calandra · 2020
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Quality and diversity in evolutionary modular robotics
Jørgen Nordmoen, Frank Veenstra, Kai Olav Ellefsen, and Kyrre Glette · 2020
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Real world morphological evolution is feasible
Tonnes F Nygaard, David Howard, and Kyrre Glette · 2020
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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 · 2020
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Efficient online estimation of empowerment for reinforcement learning
Ruihan Zhao, Pieter Abbeel, and Stas Tiomkin · 2020
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