Titlebook: Recent Advances in Reinforcement Learning; 8th European Worksho Sertan Girgin,Manuel Loth,Daniil Ryabko Conference proceedings 2008 Springe

Crayon

Reinforcement Learning with the Use of Costly Features, features that are sufficiently informative to justify their computation. We illustrate the learning behavior of our approach using a simple experimental domain that allows us to explore the effects of a range of costs on the cost-performance trade-off.

Blood-Clot

Exploiting Additive Structure in Factored MDPs for Reinforcement Learning, which cannot exploit the additive structure of a .. In this paper, we present two new instantiations of ., namely . and ., using a linear programming based planning method that can exploit the additive structure of a . and address problems out of reach of ..

一大塊

Bayesian Reward Filtering,orcement learning, as well as a specific implementation based on sigma point Kalman filtering and kernel machines. This allows us to derive an efficient off-policy model-free approximate temporal differences algorithm which will be demonstrated on two simple benchmarks.

Factorable

巨碩

狂熱語言

Lazy Planning under Uncertainty by Optimizing Decisions on an Ensemble of Incomplete Disturbance Tre number of elements. In this context, the problem of finding from an initial state .. an optimal decision strategy can be stated as an optimization problem which aims at finding an optimal combination of decisions attached to the nodes of a . modeling all possible sequences of disturbances .., ..,

Anterior

現(xiàn)任者

Algorithms and Bounds for Rollout Sampling Approximate Policy Iteration,ng as a supervised learning problem, have been proposed recently. Finding good policies with such methods requires not only an appropriate classifier, but also reliable examples of best actions, covering the state space sufficiently. Up to this time, little work has been done on appropriate covering

CLAP

果核

Regularized Fitted Q-Iteration: Application to Planning,. We propose to use fitted Q-iteration with penalized (or regularized) least-squares regression as the regression subroutine to address the problem of controlling model-complexity. The algorithm is presented in detail for the case when the function space is a reproducing-kernel Hilbert space underly