Recent Advances in Reinforcement Learning (eBook, PDF)

8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30-July 3, 2008, Revised and Selected Papers
Redaktion: Girgin, Sertan; Ryabko, Daniil; Preux, Philippe; Munos, Rémi; Loth, Manuel

• Format: PDF

Produktbeschreibung

This book constitutes revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d'Ascq, France, during June 30 - July 3, 2008.

The 21 papers presented were carefully reviewed and selected from 61 submissions. They are dedicated to the field of and current researches in reinforcement learning.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 283
• Erscheinungstermin: 27. November 2008
• Englisch
• ISBN-13: 9783540897224
• Artikelnr.: 44227316

Inhaltsangabe

Lazy Planning under Uncertainty by Optimizing Decisions on an Ensemble of Incomplete Disturbance Trees.- Exploiting Additive Structure in Factored MDPs for Reinforcement Learning.- Algorithms and Bounds for Rollout Sampling Approximate Policy Iteration.- Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case.- Regularized Fitted Q-Iteration: Application to Planning.- A Near Optimal Policy for Channel Allocation in Cognitive Radio.- Evaluation of Batch-Mode Reinforcement Learning Methods for Solving DEC-MDPs with Changing Action Sets.- Bayesian Reward Filtering.- Basis Expansion in Natural Actor Critic Methods.- Reinforcement Learning with the Use of Costly Features.- Variable Metric Reinforcement Learning Methods Applied to the Noisy Mountain Car Problem.- Optimistic Planning of Deterministic Systems.- Policy Iteration for Learning an Exercise Policy for American Options.- Tile Coding Based on Hyperplane Tiles.- Use of Reinforcement Learning in Two Real Applications.- Applications of Reinforcement Learning to Structured Prediction.- Policy Learning - A Unified Perspective with Applications in Robotics.- Probabilistic Inference for Fast Learning in Control.- United We Stand: Population Based Methods for Solving Unknown POMDPs.- New Error Bounds for Approximations from Projected Linear Equations.- Markov Decision Processes with Arbitrary Reward Processes.