Computer Science – Robotics
Scientific paper
2011-08-23
Computer Science
Robotics
Technical report accompanying an accepted paper to CDC 2011
Scientific paper
We consider the problem of finding a control policy for a Markov Decision Process (MDP) to maximize the probability of reaching some states while avoiding some other states. This problem is motivated by applications in robotics, where such problems naturally arise when probabilistic models of robot motion are required to satisfy temporal logic task specifications. We transform this problem into a Stochastic Shortest Path (SSP) problem and develop a new approximate dynamic programming algorithm to solve it. This algorithm is of the actor-critic type and uses a least-square temporal difference learning method. It operates on sample paths of the system and optimizes the policy within a pre-specified class parameterized by a parsimonious set of parameters. We show its convergence to a policy corresponding to a stationary point in the parameters' space. Simulation results confirm the effectiveness of the proposed solution.
Belta Calin A.
Ding Xu Chu
Estanjini Reza Moazzez
Lahijanian Morteza
Paschalidis Ioannis Ch.
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