State aggregation for solving Markov decision problems an application to mobile robotics

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 intractable for problems necessitating a large number of states, such as our robotics application. The use of aggregation techniques allows to reduce the number of states and our methods give nearly optimal plans in a significantly reduced time