Machine Learning

, Volume 21, Issue 3, pp 199–233

The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces

  • Andrew W. Moore
  • Christopher G. Atkeson
Article

DOI: 10.1007/BF00993591

Cite this article as:
Moore, A.W. & Atkeson, C.G. Mach Learn (1995) 21: 199. doi:10.1007/BF00993591

Abstract

Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that neither planning nor exploration occurs uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been rested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes.

Keywords

Reinforcement Learning Curse of Dimensionality Learning Control Robotics kd-trees 
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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Andrew W. Moore
    • 1
  • Christopher G. Atkeson
    • 2
  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburgh
  2. 2.Georgia Institute of TechnologyCollege of ComputingAtlanta

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