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Social learning is a key component of human and animal intelligence.
Prestige and culture: a biosocial interpretation [and comments and replies]
Barkow, J. H., Akiwowo, A. A., Barua, T. K., Chance, M., Chapple, E. D., Chattopadhyay, G. P., Freedman, D. G., Geddes, W., Goswami, B., Isichei, P., et al · 1975
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Alvinn: An autonomous land vehicle in a neural network
Pomerleau, D. A · 1989
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A framework for behavioural cloning
Bain, M. and Sammut, C · 1995
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Is imitation learning the route to humanoid robots?
Schaal, S · 1999
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Algorithms for inverse reinforcement learning
Ng, A. Y., Russell, S. J., et al · 2000
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The evolution of cultural evolution
Henrich, J. and McElreath, R · 2003
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Social learning strategies
Laland, K. N · 2004
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Learning shared latent structure for image synthesis and robotic imitation
Shon, A., Grochow, K., Hertzmann, A., and Rao, R. P · 2006
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On learning, representing, and generalizing a task in a humanoid robot
Calinon, S., Guenter, F., and Billard, A · 2007
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Bayesian inverse reinforcement learning
Ramachandran, D. and Amir, E · 2007
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Survey: Robot programming by demonstration
Billard, A., Calinon, S., Dillmann, R., and Schaal, S · 2008
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Maximum entropy inverse reinforcement learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., and Dey, A. K · 2008
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A survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., and Browning, B · 2009
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Prestige affects cultural learning in chimpanzees
Horner, V., Proctor, D., Bonnie, K. E., Whiten, A., and de Waal, F. B · 2010
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The cultural niche: Why social learning is essential for human adaptation
Boyd, R., Richerson, P. J., and Henrich, J · 2011
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A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., Gordon, G., and Bagnell, D · 2011
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
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Cooperative inverse reinforcement learning
Hadfield-Menell, D., Russell, S. J., Abbeel, P., and Dragan, A · 2016
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Reinforcement learning with unsupervised auxiliary tasks
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., and Kavukcuoglu, K · 2016
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High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P · 2016
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Loss is its own reward: Self-supervision for reinforcement learning
Shelhamer, E., Mahmoudieh, P., Argus, M., and Darrell, T · 2016
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Observational learning by reinforcement learning
Borsa, D., Heess, N., Piot, B., Liu, S., Hasenclever, L., Munos, R., and Pietquin, O · 2019
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Quantifying generalization in reinforcement learning
Cobbe, K., Klimov, O., Hesse, C., Kim, T., and Schulman, J · 2019
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Hybrid reinforcement learning with expert state sequences
Guo, X., Chang, S., Yu, M., Tesauro, G., and Campbell, M · 2019
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Agent modeling as auxiliary task for deep reinforcement learning
Hernandez-Leal, P., Kartal, B., and Taylor, M. E · 2019
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Learning from a learner
Jacq, A., Geist, M., Paiva, A., and Pietquin, O · 2019
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Prestige-biased social learning: current evidence and outstanding questions
Jiménez, Á. V. and Mesoudi, A · 2019
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Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Third-person imitation learning
Stadie, B. C., Abbeel, P., and Sutskever, I · 2017
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Learning existing social conventions via observationally augmented self-play
Lerer, A. and Peysakhovich, A · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Behavior planning of autonomous cars with social perception
Sun, L., Zhan, W., Chan, C.-Y., and Tomizuka, M · 2019
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Andrychowicz, M., Raichuk, A., Stańczyk, P., Orsini, M., Girgin, S., Marinier, R., Hussenot, L., Geist, M., Pietquin, O., Michalski, M., Gelly, S., and Bachem, O · 2020
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