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CORR
2010
Springer
105views Education» more  CORR 2010»
13 years 7 months ago
Optimism in Reinforcement Learning Based on Kullback-Leibler Divergence
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
Sarah Filippi, Olivier Cappé, Aurelien Gari...
AAAI
2010
13 years 10 months ago
Representation Discovery in Sequential Decision Making
Automatically constructing novel representations of tasks from analysis of state spaces is a longstanding fundamental challenge in AI. I review recent progress on this problem for...
Sridhar Mahadevan
CAV
2009
Springer
156views Hardware» more  CAV 2009»
14 years 3 months ago
Towards Performance Prediction of Compositional Models in Industrial GALS Designs
Systems and Networks on Chips (NoCs) are a prime design focus of many hardware manufacturers. In addition to functional verification, which is a difficult necessity, the chip desi...
Nicolas Coste, Holger Hermanns, Etienne Lantreibec...
JSAC
2008
95views more  JSAC 2008»
13 years 7 months ago
Cognitive Medium Access: Constraining Interference Based on Experimental Models
In this paper we design a cognitive radio that can coexist with multiple parallel WLAN channels while abiding by an interference constraint. The interaction between both systems is...
Stefan Geirhofer, Lang Tong, Brian M. Sadler
PKDD
2009
Springer
129views Data Mining» more  PKDD 2009»
14 years 3 months ago
Considering Unseen States as Impossible in Factored Reinforcement Learning
Abstract. The Factored Markov Decision Process (FMDP) framework is a standard representation for sequential decision problems under uncertainty where the state is represented as a ...
Olga Kozlova, Olivier Sigaud, Pierre-Henri Wuillem...