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Model-Based Reinforcement Learning

Model-Based Reinforcement Learning

This video was recorded at 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009. In model-based reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. This tutorial will survey work in this area with an emphasis on recent results. Topics will include: Efficient learning in the PAC-MDP formalism, Bayesian reinforcement learning, models and linear function approximation, recent advances in planning.
Keywords:
videolectures, ocwc, oec
Disciplines:
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More about this material

Material Type:
Presentation
Date Added to MERLOT:
February 10, 2015
Date Modified in MERLOT:
February 10, 2015
Author:
Submitter:
The Open Education Consortium
Primary Audience:
College General Ed, College Lower Division, College Upper Division
Technical Format:
Video
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
No
Accessibility Information Available:
Unknown
Creative Commons:
Creative Commons License
This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States

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