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SUMMARY:Optimal Bayesian Reinforcement Learning on Trees - Philipp Hennig 
 (University of Cambridge)
DTSTART:20090518T140000Z
DTEND:20090518T150000Z
UID:TALK18485@talks.cam.ac.uk
CONTACT:Philipp Hennig
DESCRIPTION:The "Q-Learning" algorithm is the classical solution to the so
 -called "Optimal" Reinforcement Learning Problem. Q-Learning uses samples 
 of future rewards generated by a non-optimal policy to derive point estima
 tes of the future rewards from the (unknown) optimal policy. \n\nIn the fi
 rst part of this talk\, I will show that a Bayesian treatment\, in forcing
  us to explicitly define our assumptions\, reveals some interesting aspect
 s of this problem that seem to have been overlooked so far. \n\nIn the sec
 ond part\, I will introduce an algorithm that uses Expectation Propagation
  to generate beliefs over possible future rewards from the optimal policy 
 if the Markov Environment forms a tree (i.e. "Bayesian Q-Learning on trees
 ") and will show some preliminary results for its application to Game Tree
 s. 
LOCATION:TCM Seminar Room\, Cavendish Laboratory\, Department of Physics
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