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There is a growing interest in developing automated agents that can work alongside humans.
A planning heuristic based on causal graph analysis
Helmert, M · 2004
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Curl: Contrastive unsupervised representations for reinforcement learning, 2020
Srinivas, A., Laskin, M., and Abbeel, P · 2004
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Interactively shaping agents via human reinforcement: The tamer framework
Knox, W. B. and Stone, P · 2009
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Active learning literature survey
Settles, B · 2009
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A concise introduction to models and methods for automated planning
Geffner, H. and Bonet, B · 2013
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Causal inference in statistics: A primer
Glymour, M., Pearl, J., and Jewell, N. P · 2016
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Modular multitask reinforcement learning with policy sketches
Andreas, J., Klein, D., and Levine, S · 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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Inverse reward design
Hadfield-Menell, D., Milli, S., Abbeel, P., Russell, S. J., and Dragan, A · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
The contextual loss for image transformation with non-aligned data
Mechrez, R., Talmi, I., and Zelnik-Manor, L · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
Cited alongside, same era.
Deep tamer: Interactive agent shaping in high-dimensional state spaces
Warnell, G., Waytowich, N., Lawhern, V., and Stone, P · 2018
Cited alongside, same era.
Symbolic plans as high-level instructions for reinforcement learning
Illanes, L., Yan, X., Icarte, R. T., and McIlraith, S. A · 2020
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Active learning for skewed data sets
Kazerouni, A., Zhao, Q., Xie, J., Tata, S., and Najork, M · 2020
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Sreedharan, S., Soni, U., Verma, M., Srivastava, S., and Kambhampati, S · 2020
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Understanding and simplifying perceptual distances
Amir, D. and Weiss, Y · 2021
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Lee, K., Smith, L., and Abbeel, P · 2021
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Yang, F., Lyu, D., Liu, B., and Gustafson, S · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
Cited alongside, same era.
Sdrl: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning
Lyu, D., Yang, F., Liu, B., and Gustafson, S · 2019
Cited alongside, same era.
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Leveraging approximate symbolic models for reinforcement learning via skill diversity
Guan, L., Sreedharan, S., and Kambhampati, S · 2022
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Reward machines: Exploiting reward function structure in reinforcement learning
Icarte, R. T., Klassen, T. Q., Valenzano, R., and McIlraith, S. A · 2022
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Symbols as a lingua franca for bridging human-ai chasm for explainable and advisable ai systems
Kambhampati, S., Sreedharan, S., Verma, M., Zha, Y., and Guan, L · 2022
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