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Designers of AI agents often iterate on the reward function in a trial-and-error process until they get the desired behavior, but this only guarantees good behavior in the training environment.
Algorithms for inverse reinforcement learning
Ng, A. Y. and Russell, S. J. (2000) · 2000
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Bayesian inverse reinforcement learning
Ramachandran, D. and Amir, E. (2007) · 2007
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Maximum entropy inverse reinforcement learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., and Dey, A. K. (2008) · 2008
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Interactively shaping agents via human reinforcement: The TAMER framework
Knox, W. B. and Stone, P. (2009) · 2009
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Where do rewards come from?
Singh, S., Lewis, R. L., and Barto, A. G. (2009) · 2009
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Preference learning
Fürnkranz, J. and Hüllermeier, E. (2010) · 2010
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Bayesian active learning for classification and preference learning
Houlsby, N., Huszar, F., Ghahramani, Z., and Lengyel, M. (2011) · 2011
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APRIL: Active preference learning-based reinforcement learning
Akrour, R., Schoenauer, M., and Sebag, M. (2012) · 2012
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Preference-based reinforcement learning: a formal framework and a policy iteration algorithm
Fürnkranz, J., Hüllermeier, E., Cheng, W., and Park, S.-H. (2012) · 2012
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Active reward learning
Daniel, C., Viering, M., Metz, J., Kroemer, O., and Peters, J. (2014) · 2014
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Preference learning for move prediction and evaluation function approximation in othello
Runarsson, T. P. and Lucas, S. M. (2014) · 2014
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Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D. (2016) · 2016
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Value iteration networks
Tamar, A., WU, Y., Thomas, G., Levine, S., and Abbeel, P. (2016) · 2016
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Model-free preference-based reinforcement learning
Deep Bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z. (2017) · 2017
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Inverse reward design
Hadfield-Menell, D., Milli, S., Abbeel, P., Russell, S. J., and Dragan, A. (2017) · 2017
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Active preference-based learning of reward functions
Sadigh, D., Dragan, A., Sastry, S., and Seshia, S. A. (2017) · 2017
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Deep TAMER: interactive agent shaping in high-dimensional state spaces
Warnell, G., Waytowich, N. R., Lawhern, V., and Stone, P. (2017) · 2017
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A survey of preference-based reinforcement learning methods
Wirth, C., Akrour, R., Neumann, G., and Fürnkranz, J. (2017) · 2017
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Wirth, C., Furnkranz, J., Neumann, G., et al. (2016) · 2016
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Repeated inverse reinforcement learning
Amin, K., Jiang, N., and Singh, S. (2017) · 2017
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Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D. (2017) · 2017
Cited alongside, same era.
Andrychowicz, M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., et al. (2018) · 2018
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Learning from richer human guidance: Augmenting comparison-based learning with feature queries
Basu, C., Singhal, M., and Dragan, A. D. (2018) · 2018
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Active reward learning from critiques
Cui, Y. and Niekum, S. (2018) · 2018
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