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Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems.
Learning Agents for Uncertain Environments (Extended Abstract)
Stuart Russell · 1998
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y. Ng, Daishi Harada, and Stuart J. Russell · 1999
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Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart J. Russell · 2000
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On the Sample Complexity of Reinforcement Learning
Sham M. Kakade · 2003
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y. Ng · 2004
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Bayesian inverse reinforcment learning
Deepak Ramachandran and Eyal Amir · 2007
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Adam: A method for stochastic optimization
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R.S. Sutton and A.G. Barto · 2018
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Identifiability in inverse reinforcement learning
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Unpacking reward shaping: Understanding the benefits of reward engineering on sample complexity
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Guaranteeing control requirements via reward shaping in reinforcement learning
Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, and Mario di Bernardo · 2023
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Bridging RL Theory and Practice with the Effective Horizon
Cassidy Laidlaw, Stuart Russell, and Anca Dragan · 2023
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Yunlong Dong, Xiuchuan Tang, and Ye Yuan · 2020
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Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
Yujing Hu, Weixun Wang, Hangtian Jia, Yixiang Wang, Yingfeng Chen, Jianye Hao, Feng Wu, and Changjie Fan · 2020
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Maximum entropy inverse reinforcement learning
Brian D. Ziebart, Andrew Maas, J. Andrew Bagnell, and Anind K. Dey · 2023
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