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Reinforcement learning is typically concerned with learning control policies tailored to a particular agent.
A phylogenetic analysis of behavioral neuro-ontogeny in precocial and nonprecocial mammals
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Evolving virtual creatures
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Practical parameterization of rotations using the exponential map
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Patterns of development: the altricial-precocial spectrum
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Loopy belief propagation for approximate inference: An empirical study
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How animals move: an integrative view
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Principles of neural science
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Central pattern generators and the control of rhythmic movements
Marder, E. and Bucher, D · 2001
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Combinations of muscle synergies in the construction of a natural motor behavior
d’Avella, A., Saltiel, P., and Bizzi, E · 2003
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The dynamics of legged locomotion: Models, analyses, and challenges
Holmes, P., Full, R. J., Koditschek, D., and Guckenheimer, J · 2006
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Visualizing data using t-sne
Maaten, L. v. d. and Hinton, G · 2008
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Equine Locomotion-E-Book
Back, W. and Clayton, H. M · 2013
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Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
Cheney, N., MacCurdy, R., Clune, J., and Lipson, H · 2014
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Modularity in motor control: from muscle synergies to cognitive action representation
d’Avella, A., Giese, M., Ivanenko, Y. P., Schack, T., and Flash, T · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Neural module networks
Andreas, J., Rohrbach, M., Darrell, T., and Klein, D · 2016
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Learning to communicate with deep multi-agent reinforcement learning
Foerster, J., Assael, I. A., De Freitas, N., and Whiteson, S · 2016
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End-to-end training of deep visuomotor policies
Levine, S., Finn, C., Darrell, T., and Abbeel, P · 2016
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2016
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Hardware conditioned policies for multi-robot transfer learning
Chen, T., Murali, A., and Gupta, A · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Addressing function approximation error in actor-critic methods
Fujimoto, S., Van Hoof, H., and Meger, D · 2018
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Emergence of grounded compositional language in multi-agent populations
Mordatch, I. and Abbeel, P · 2018
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Deep online learning via meta-learning: Continual adaptation for model-based rl
Nagabandi, A., Finn, C., and Levine, S · 2018
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Learning multiagent communication with backpropagation
Sukhbaatar, S., Fergus, R., et al · 2016
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Continuous adaptation via meta-learning in nonstationary and competitive environments
Al-Shedivat, M., Bansal, T., Burda, Y., Sutskever, I., Mordatch, I., and Abbeel, P · 2017
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Openai baselines
Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Sidor, S., Wu, Y., and Zhokhov, P · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Joint optimization of robot design and motion parameters using the implicit function theorem
Ha, S., Coros, S., Alspach, A., Kim, J., and Yamane, K · 2017
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Emergence of locomotion behaviours in rich environments
Heess, N., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, A., Riedmiller, M., et al · 2017
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nearai/torchfold: v0.1.0, Jun 2018
Polosukhin, I. and Zavershynskyi, M · 2018
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Graph networks as learnable physics engines for inference and control
Sanchez-Gonzalez, A., Heess, N., Springenberg, J. T., Merel, J., Riedmiller, M., Hadsell, R., and Battaglia, P · 2018
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Jointly learning to construct and control agents using deep reinforcement learning
Schaff, C., Yunis, D., Chakrabarti, A., and Walter, M. R · 2018
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Nervenet: Learning structured policy with graph neural networks
Wang, T., Liao, R., Ba, J., and Fidler, S · 2018
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Solving rubik’s cube with a robot hand
Akkaya, I., Andrychowicz, M., Chociej, M., Litwin, M., McGrew, B., Petron, A., Paino, A., Plappert, M., Powell, G., Ribas, R., et al · 2019
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Emergent tool use from multi-agent autocurricula
Baker, B., Kanitscheider, I., Markov, T., Wu, Y., Powell, G., McGrew, B., and Mordatch, I · 2019
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Learning to control self-assembling morphologies: A study of generalization via modularity
Pathak, D., Lu, C., Darrell, T., Isola, P., and Efros, A · 2019
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Neural graph evolution: Towards efficient automatic robot design
Wang, T., Zhou, Y., Fidler, S., and Ba, J · 2019
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Dynamic graph message passing networks
Zhang, L., Xu, D., Arnab, A., and Torr, P. H · 2019
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