IRCS Technical Reports Series
Document Type
Technical Report
Date of this Version
December 1993
Abstract
We describe a density-adaptive reinforcement learning and a density-adaptive forgetting algorithm. This learning algorithm uses hybrid k-D/2k-trees to allow for a variable resolution partitioning and labelling of the input space. The density adaptive forgetting algorithm deletes observations from the learning set depending on whether subsequent evidence is available in a local region of the parameter space. The algorithms are demonstrated in a simulation for learning feasible robotic grasp approach directions and orientations and then adapting to subsequent mechanical failures in the gripper.
COinS
Date Posted: 20 September 2006
Comments
University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-93-50.