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AAAI
1998
15 years 2 months ago
Applying Online Search Techniques to Continuous-State Reinforcement Learning
In this paper, we describe methods for e ciently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boo...
Scott Davies, Andrew Y. Ng, Andrew W. Moore
107
Voted
ICANN
2010
Springer
15 years 1 months ago
Exploring Continuous Action Spaces with Diffusion Trees for Reinforcement Learning
We propose a new approach for reinforcement learning in problems with continuous actions. Actions are sampled by means of a diffusion tree, which generates samples in the continuou...
Christian Vollmer, Erik Schaffernicht, Horst-Micha...
GECON
2008
Springer
134views Business» more  GECON 2008»
15 years 1 months ago
Rational Bidding Using Reinforcement Learning
The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of arti...
Nikolay Borissov, Arun Anandasivam, Niklas Wirstr&...
NN
2006
Springer
127views Neural Networks» more  NN 2006»
15 years 21 days ago
The asymptotic equipartition property in reinforcement learning and its relation to return maximization
We discuss an important property called the asymptotic equipartition property on empirical sequences in reinforcement learning. This states that the typical set of empirical seque...
Kazunori Iwata, Kazushi Ikeda, Hideaki Sakai
PKDD
2010
Springer
129views Data Mining» more  PKDD 2010»
14 years 11 months ago
Smarter Sampling in Model-Based Bayesian Reinforcement Learning
Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
Pablo Samuel Castro, Doina Precup