[Lisa_seminaires] REMINDER: UdeM-McGill-MITACS machine learning seminar Fri Nov. 16, 11:30am, MC 437
Hugo Larochelle
larocheh at IRO.UMontreal.CA
Jeu 15 Nov 12:00:10 EST 2007
This week's seminar (see http://www.iro.umontreal.ca/article.php3?
id_article=107&lang=en):
Bayesian Reinforcement Learning
by Mohammad Ghavamzadeh,
Department of Computing Science
University of Alberta
Location: McConnell Building (McGill), room 437
Time: November 16th 2007, 11h30
Reinforcement learning is a class of learning problems in which an
agent interacts with an unfamiliar, dynamic and stochastic
environment, and whose goal is to optimize some measure of its long-
term performance. Despite extensive research and numerous successes
in a number of different domains, there remain several fundamental
obstacles hindering the widespread application of reinforcement
learning methodology to real-world problems. Recent advances have
shown that Bayesian approach to reinforcement learning offers viable
solutions to some of these major limitations, such as the lack of
confidence intervals for performance predictions, the difficulty of
appropriately reconciling exploration with exploitation, and the lack
of a systematic method for encoding prior knowledge and for
formulating domain assumptions.
Policy gradient methods are reinforcement learning algorithms that
adapt a parameterized policy by following a performance gradient
estimate. This talk will present two Bayesian policy gradient
algorithms. These algorithms use Gaussian processes to define prior
distribution over the performance gradient, and obtain closed-form
expressions for its posterior distribution, conditioned on the
observed data. The posterior mean serves as the policy gradient
estimate and is used to update the policy, while the posterior
covariance allows us to gauge the reliability of the update. In the
first algorithm, the basic observable unit, upon which learning and
inference are based, is a complete trajectory, allowing the algorithm
to handle non-Markovian systems. The second algorithm takes advantage
of the Markov property of the system trajectories and uses individual
state-action-reward transitions as its basic observable unit. This
helps reduce variance in the gradient estimates and facilitates
handling continuing problems.
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