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ICML
2007
IEEE

Bayesian actor-critic algorithms

9 years 12 months ago
Bayesian actor-critic algorithms
We1 present a new actor-critic learning model in which a Bayesian class of non-parametric critics, using Gaussian process temporal difference learning is used. Such critics model the state-action value function as a Gaussian process, allowing Bayes' rule to be used in computing the posterior distribution over state-action value functions, conditioned on the observed data. Appropriate choices of the prior covariance (kernel) between stateaction values and of the parametrization of the policy allow us to obtain closed-form expressions for the posterior distribution of the gradient of the average discounted return with respect to the policy parameters. The posterior mean, which serves as our estimate of the policy gradient, is used to update the policy, while the posterior covariance allows us to gauge the reliability of the update.
Mohammad Ghavamzadeh, Yaakov Engel
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Mohammad Ghavamzadeh, Yaakov Engel
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