Reinforcement learning with misspecified model classes

Reinforcement learning with misspecified model classes

Real-world robots commonly have to act in complex, poorly understood environments where the true world dynamics are unknown. To compensate for the unknown world dynamics, we often provide a class of models to a learner so it may select a model, typically using a minimum prediction error metric over...

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Bibliographic Details
Main Authors: Joseph, Joshua Mason (Contributor), Geramifard, Alborz (Contributor), Roberts, John W. (Contributor), How, Jonathan P. (Contributor), Roy, Nicholas (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2015-05-08T18:36:13Z.
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