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Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach
 

Summary: Nonparametric Representation of Policies and
Value Functions: A Trajectory-Based Approach
Christopher G. Atkeson

Robotics Institute and HCII
Carnegie Mellon University
Pittsburgh, PA 15213, USA
cga@cmu.edu
Jun Morimoto
ATR Human Information Science Laboratories, Dept. 3
Keihanna Science City
Kyoto 619-0288, Japan
xmorimo@atr.co.jp
Abstract
A longstanding goal of reinforcement learning is to develop non-
parametric representations of policies and value functions that support
rapid learning without suffering from interference or the curse of di-
mensionality. We have developed a trajectory-based approach, in which
policies and value functions are represented nonparametrically along tra-
jectories. These trajectories, policies, and value functions are updated as

  

Source: Atkeson, Christopher G. - Robotics Institute, School of Computer Science, Carnegie Mellon University
Carnegie Mellon University, School of Computer Science, Informedia Project
Zeglin, Garth - Robotics Institute, Carnegie Mellon University

 

Collections: Computer Technologies and Information Sciences; Engineering