Variable Resolution Reinforcement Learning.

Abstract

Can reinforcement learning ever become a practical method for real control problems? This paper begins by reviewing three reinforcement learning algorithms to study their shortcomings and to motivate subsequent improvements. By assuming that paths must be continuous, we can substantially reduce the proportion of state space which the learning algorithms need explore. Next, we introduce the partigame algorithm for variable resolution reinforcement learning. In addition to exploring state space, and developing a control policy to achieve a task, partigame also learns a kd-tree partitioning of state space. Some experiments are described which show partigame in operation on a non-linear dynamics problems and a path learning planning task in a 9-dimensional configuration space.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1995
Accession Number
ADA311507

Entities

People

  • Andrew W. Moore

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Dynamics
  • Learning
  • Nonlinear Dynamics
  • Physics
  • Reinforcement Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space
  • Space - Spacecraft Maneuvers