36 search results - page 1 / 8 » Using Linear Programming for Bayesian Exploration in Markov ... |
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 ...
SARA
2007
Springer
13 years 11 months ago
2007
Springer
Several recent techniques for solving Markov decision processes use dynamic Bayesian networks to compactly represent tasks. The dynamic Bayesian network representation may not be g...
ICML
2010
IEEE
13 years 6 months ago
2010
IEEE
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
UAI
2004
13 years 6 months ago
2004
We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamica...
ECAI
2010
Springer
13 years 6 months ago
2010
Springer
A Markov Decision Process (MDP) is a general model for solving planning problems under uncertainty. It has been extended to multiobjective MDP to address multicriteria or multiagen...