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ECML
2005
Springer
14 years 28 days ago
Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes
Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actio...
Masoumeh T. Izadi, Doina Precup
ICML
2006
IEEE
14 years 8 months ago
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
Recent decision-theoric planning algorithms are able to find optimal solutions in large problems, using Factored Markov Decision Processes (fmdps). However, these algorithms need ...
Thomas Degris, Olivier Sigaud, Pierre-Henri Wuille...
ECAI
2004
Springer
14 years 23 days ago
On-Line Search for Solving Markov Decision Processes via Heuristic Sampling
In the past, Markov Decision Processes (MDPs) have become a standard for solving problems of sequential decision under uncertainty. The usual request in this framework is the compu...
Laurent Péret, Frédérick Garc...
ICML
2005
IEEE
14 years 8 months ago
Coarticulation: an approach for generating concurrent plans in Markov decision processes
We study an approach for performing concurrent activities in Markov decision processes (MDPs) based on the coarticulation framework. We assume that the agent has multiple degrees ...
Khashayar Rohanimanesh, Sridhar Mahadevan
ICTAI
2000
IEEE
13 years 11 months ago
Building efficient partial plans using Markov decision processes
Markov Decision Processes (MDP) have been widely used as a framework for planning under uncertainty. They allow to compute optimal sequences of actions in order to achieve a given...
Pierre Laroche