The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Bayesian Inverse Reinforcement Learning
Abstract
Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an expert. IRL is motivated by situations where knowledge of the rewards is a goal by itself (as in preference elicitation) and by the task of apprenticeship learning (learning policies from an expert). In this paper we show how to combine prior knowledge and evidence from the expert's actions to derive a probability distribution over the space of reward functions. We present efficient algorithms that find solutions for the reward learning and apprenticeship learning tasks that generalize well over these distributions. Experimental results show strong improvement for our methods over previous heuristic-based approaches.
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Cites
Linear Matrix Inequalities in System and Control Theory
Boyd, S., El Ghaoui, L., Feron, E., & Balakrishnan, V. (1994) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970777 · Cited by 21,687
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Berger, J. O. (1985) · Springer Series in Statistics · doi:10.1007/978-1-4757-4286-2 · Cited by 7,722
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855
Inverse Problem Theory and Methods for Model Parameter Estimation
Tarantola, A. (2005) · doi:10.1137/1.9780898717921 · Cited by 6,585
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Apprenticeship learning via inverse reinforcement learning
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(2020) · Machine Learning and Visual Perception · doi:10.1515/9783110595567-011 · Cited by 1,930
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An Introduction to the Ising Model
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Cited by
Maximum Entropy Inverse Reinforcement Learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., & Dey, A. K. (2018) · doi:10.1184/r1/6555512 · Cited by 1,869
Autonomous Helicopter Aerobatics through Apprenticeship Learning
Abbeel, P., Coates, A., & Ng, A. Y. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910371999 · Cited by 584
Planning-based prediction for pedestrians
Ziebart, B. D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J. A., Hebert, M., Dey, A. K., & Srinivasa, S. (2009) · 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2009.5354147 · Cited by 456
An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
Ganesh, A. H., & Xu, B. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111833 · Cited by 285
Metadata
- Venue
- Partially Observed Markov Decision Processes
- Publisher
- Cambridge University Press
- Published
- 2025-05-31
- Pages
- 549-569
- OpenAlex
- W1591675293
- Cited by
- 444
- References
- 14
- FWCI
- 16.86
- Type
- book chapter
- Language
- en
- Open access
- closed
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