Reference : Bayesian Reinforcement Learning
Parts of books : Contribution to collective works
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/3390
Bayesian Reinforcement Learning
English
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Ghavamzadeh, Mohammad [> >]
Mannor, Shie [> >]
Poupart, Pascal [> >]
2012
Reinforcement Learning: State of the Art
Wiering, Marco
van Otterlo, Martijn
Springer
359-386
Yes
978-3-642-27645-3
[en] This chapter surveys recent lines of work that use Bayesian techniques for reinforcement learning. In Bayesian learning, uncertainty is expressed by a prior distribution over unknown parameters and learning is achieved by computing a posterior distribution based on the data observed. Hence, Bayesian reinforcement learning distinguishes itself from other forms of reinforcement learning by explic- itly maintaining a distribution over various quantities such as the parameters of the model, the value function, the policy or its gradient. This yields several benefits: a) domain knowledge can be naturally encoded in the prior distribution to speed up learning; b) the exploration/exploitation tradeoff can be naturally optimized; and c) notions of risk can be naturally taken into account to obtain robust policies.
Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group)
http://hdl.handle.net/10993/3390
also: http://hdl.handle.net/10993/11028
10.1007/978-3-642-27645-3_11
http://link.springer.com/chapter/10.1007/978-3-642-27645-3_11

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