9 search results - page 1 / 2 » Bayesian sparse sampling for on-line reward optimization |
ICML
2005
IEEE
14 years 5 months ago
2005
IEEE
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
IJCAI
2007
13 years 6 months ago
2007
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
AAAI
2011
12 years 4 months ago
2011
Planning agents often lack the computational resources needed to build full planning trees for their environments. Agent designers commonly overcome this finite-horizon approxima...
SIAMIS
2011
12 years 11 months ago
2011
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
We consider the exploration/exploitation problem in reinforcement learning (RL). The Bayesian approach to model-based RL offers an elegant solution to this problem, by considering...