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Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unknown or cannot easily be used for training.
Active Learning: Theory and Applications
S. Tong · 2001
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Back to reality: Crossing the reality gap in evolutionary robotics
J. C. Zagal, J. Ruiz-del Solar, and P. Vallejos · 2004
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Once more unto the breach: Co-evolving a robot and its simulator
J. Bongard and H. Lipson · 2004
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Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy
B. D. Ziebart · 2010
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E. Brochu, V. M. Cora, and N. de Freitas · 2010
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Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Bayesian optimization in high dimensions via random embeddings
Z. Wang, M. Zoghi, F. Hutter, D. Matheson, and N. De Freitas · 2013
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Poppy: open-source, 3D printed and fully-modular robotic platform for science, art and education
M. Lapeyre · 2014
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Adam: A method for stochastic optimization, 2014
D. P. Kingma and J. Ba · 2014
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Bullet physics simulation
E. Coumans · 2015
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Q. Liu and D. Wang · 2016
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Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. C. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2016
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(cad)$ˆ2$rl: Real single-image flight without a single real image
F. Sadeghi and S. Levine · 2016
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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Sim-to-real transfer of robotic control with dynamics randomization
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
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Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox · 2018
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Learning to simulate, 2018
N. Ruiz, S. Schulter, and M. Chandraker · 2018
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Vadra: Visual adversarial domain randomization and augmentation
R. Khirodkar, D. Yoo, and K. M. Kitani · 2018
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Which training methods for GANs do actually converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
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Stein variational policy gradient, 2017
Y. Liu, P. Ramachandran, Q. Liu, and J. Peng · 2017
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Robust adversarial reinforcement learning, 2017
L. Pinto, J. Davidson, R. Sukthankar, and A. Gupta · 2017
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Learning dexterous in-hand manipulation
M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, et al · 2018
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Reinforcement Learning: An introduction
R. S. Sutton and A. G. Barto · 2018
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Diversity is all you need: Learning skills without a reward function
B. Eysenbach, A. Gupta, J. Ibarz, and S. Levine · 2018
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Composable deep reinforcement learning for robotic manipulation
T. Haarnoja, V. Pong, A. Zhou, M. Dalal, P. Abbeel, and S. Levine · 2018
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Sim-to-real transfer with neural-augmented robot simulation
F. Golemo, A. A. Taiga, A. Courville, and P.-Y. Oudeyer · 2018
Cited alongside, same era.
M. Toneva, A. Sordoni, R. T. d. Combes, A. Trischler, Y. Bengio, and G. J. Gordon · 2018
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Assessing generalization in deep reinforcement learning, 2018
C. Packer, K. Gao, J. Kos, P. Krähenbühl, V. Koltun, and D. Song · 2018
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Quantifying generalization in reinforcement learning
K. Cobbe, O. Klimov, C. Hesse, T. Kim, and J. Schulman · 2018
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Generalization and regularization in DQN, 2018
J. Farebrother, M. C. Machado, and M. Bowling · 2018
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Addressing function approximation error in actor-critic methods
S. Fujimoto, H. van Hoof, and D. Meger · 2018
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Learning self-imitating diverse policies
T. Gangwani, Q. Liu, and J. Peng · 2019
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