State Aggregation for Solving Markov Decision Problems - An Application to Mobile Robotics
Résumé
In this paper, we present two state aggregation methods, used to build stochastic plans, modelling our environment with Markov Decision Processes. Classical methods used to compute stochastic plans are highly untractable for problems necessiting a large number of states, like our robotics application. The use of aggregation techniques allows to reduce the number of states to take into account, and our methods give nearly optimal plans in a significantly reduced time.