Seminar on Computational Learning and
Adaptation
A Connectionist Approach to Reinforcement Learning for Robotic Control: The Advantages of Indexed Partitioning
Maria L. Gini
Department of Computer Science and Engineering,
University of Minnesota
Minneapolis, MN
gini@tc.umn.edu
We explore the use of a connectionist-learning system designed
to allow the application of reinforcement learning to robotic
control. In particular, we compare direct and indexed partitioning
methods and find that indexed partitioning has advantages in time
complexity, space complexity, learning speed (measured in trials),
and success rate.
As application domain we chose the problem of learning to back a
truck and trailer rig to a target location by steering the front
wheels of the truck. We present extensive simulation results and
results from runs on a real robot which learns on-line.
We describe some of the subtle difficulties we faced in transferring
our algorithm from simulation to a real robot. In simulation we
obtained a good success rate when using a uniform distribution for
the initial configuration of the truck and trailer rig. This proved
to be very difficult to achieve with the real robot, and required us
to build a robot with a self-positioning mechanism which is capable
of achieving any desired initial configuration.
Date: Thurs., May 18
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Time: 4:15-5:30PM
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Place: Cordura 100
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