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In many real-world applications, the reward function is too complex to be manually specified.
Generalization in reinforcement learning: Safely approximating the value function
Justin Boyan and Andrew W Moore · 1995
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y. Ng, Daishi Harada, and Stuart Russell · 1999
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Algorithms for inverse reinforcement learning
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Bayesian inverse reinforcement learning
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Active preference-based learning of reward functions
Dorsa Sadigh, Anca D. Dragan, S. Shankar Sastry, and Sanjit A. Seshia · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2018
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Preferences implicit in the state of the world
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Fine-tuning language models from human preferences
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Explainable reinforcement learning through a causal lens
Prashan Madumal, Tim Miller, Liz Sonenberg, and Frank Vetere · 2020
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Understanding learned reward functions
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Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
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Explainable reinforcement learning: A survey
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Explicable reward design for reinforcement learning agents
Rati Devidze, Goran Radanovic, Parameswaran Kamalaruban, and Adish Singla · 2021
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Causal analysis of agent behavior for AI safety
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Invariance in policy optimisation and partial identifiability in reward learning, 2022
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