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As humans interact with autonomous agents to perform increasingly complicated, potentially risky tasks, it is important to be able to efficiently evaluate an agent's performance and correctness.
Introduction to reinforcement learning , volume 135
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High-confidence off-policy evaluation
Thomas, P. S., Theocharous, G., and Ghavamzadeh, M · 2015
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Towards resolving unidentifiability in inverse reinforcement learning
Amin, K. and Singh, S · 2016
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Cooperative inverse reinforcement learning
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Russell, S. J. and Norvig, P · 2016
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Amin, K., Jiang, N., and Singh, S · 2017
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Barreto, A., Dabney, W., Munos, R., Hunt, J. J., Schaul, T., van Hasselt, H. P., and Silver, D · 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
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Hanna, J. P., Stone, P., and Niekum, S · 2017
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Huang, S. H., Bhatia, K., Abbeel, P., and Dragan, A. D · 2018
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Scalable agent alignment via reward modeling: a research direction
Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, V., and Legg, S · 2018
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Asking easy questions: A user-friendly approach to active reward learning
Bıyık, E., Palan, M., Landolfi, N. C., Losey, D. P., and Sadigh, D · 2019
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Brown, D. S. and Niekum, S · 2019
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