The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Information theoretic MPC for model-based reinforcement learning
Abstract
We introduce an information theoretic model predictive control (MPC) algorithm capable of handling complex cost criteria and general nonlinear dynamics. The generality of the approach makes it possible to use multi-layer neural networks as dynamics models, which we incorporate into our MPC algorithm in order to solve model-based reinforcement learning tasks. We test the algorithm in simulation on a cart-pole swing up and quadrotor navigation task, as well as on actual hardware in an aggressive driving task. Empirical results demonstrate that the algorithm is capable of achieving a high level of performance and does so only utilizing data collected from the system.
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Citations
Cites
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Cited by
Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J.-M., Lam, V.-D., Bewley, A., & Shah, A. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793742 · Cited by 694
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1805.12114 · Cited by 525
Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning
Nagabandi, A., Clavera, I., Liu, S., Fearing, R. S., Abbeel, P., Levine, S., & Finn, C. (2018) · arXiv · doi:10.48550/arxiv.1803.11347 · Cited by 370
Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing
Betz, J., Zheng, H., Liniger, A., Rosolia, U., Karle, P., Behl, M., Krovi, V., & Mangharam, R. (2022) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2022.3181510 · Cited by 322
Agile Autonomous Driving using End-to-End Deep Imitation Learning
Pan, Y., Cheng, C.-A., Saigol, K., Lee, K., Yan, X., Theodorou, E., & Boots, B. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.056 · Cited by 257
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Metadata
- Authors
- Williams, G., Wagener, N., Goldfain, B., Drews, P., Rehg, J. M., Boots, B., & Theodorou, E. A.
- Venue
- 2017 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 1714-1721
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2738778707
- Cited by
- 460
- References
- 1
- FWCI
- 54.62
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Williams, G., Wagener, N., Goldfain, B., Drews, P., Rehg, J. M., Boots, B., & Theodorou, E. A. (2017). Information theoretic MPC for model-based reinforcement learning. 2017 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2017.7989202
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