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...
Time is a crucial variable in planning and often requires special attention since it introduces a specific structure along with additional complexity, especially in the case of dec...
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...
Factored Reinforcement Learning (frl) is a new technique to solve Factored Markov Decision Problems (fmdps) when the structure of the problem is not known in advance. Like Anticipa...
Olivier Sigaud, Martin V. Butz, Olga Kozlova, Chri...
Abstract--Feature selection is an important challenge in machine learning. Unfortunately, most methods for automating feature selection are designed for supervised learning tasks a...