This paper describes a framework for controlling autonomous agents. We estimate an optimal controller using a novel reinforcement learning method based on stochastic optimization. The agent's skeletal configurations are taken from a motion graph which contains seamless transitions, guaranteeing smooth, natural-looking motion. The controller learns a parametric value function for choosing transitions at the branch points in the motion graph. Since this query can be completed quickly, synthesis is performed online in real-time. We couple the local controller with a global path planner to create a system which produces realistic motion even in a rapidly changing environment.
Title
Learning to Move Autonomously in a Hostile World
Published
2005-06-01
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
CSD-05-1395
Type
Text
Extent
6 p
Archive
The Engineering Library
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