|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Bayesian Reinforcement Learning
If you have a question about this talk, please contact Sinead Williamson.
Milica Gasic and Yunus Saatci will present on Bayesian Reinforcement learning.
Reading: A skim of Chapters 3 and 4 of Sutton and Barto and a skim of the following papers: A Bayesian Framework for Reinforcement Learning, An Analytic Solution to Discrete Bayesian Reinforcement Learning (Poupart et al., 2006), Reinforcement Learning with Gaussian Processes, Bayesian Policy Gradient
This talk is part of the Machine Learning Reading Group @ CUED series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsCU Underwater Exploration Group Spring School 2010 - 'Axon-Glia Biology in Health and Disease' CTR Seminar Series
Other talksPICO PROJECT – NO MORE PASSWORDS Efficient Constrained Inference and Structured Neural Networks for Semantic Role Labeling Modelling Quantum Mechanics with Machine Learning: Interatomic Potentials with Error Bars Dr Juan Valcarcel - Title tbc Perspectives in Nano Information Processing 'Humanitarian Action in Armed Conflict'