ISSN:
0885-6125
Keywords:
Reinforcement Learning
;
Curse of Dimensionality
;
Learning Control
;
Robotics
;
kd-trees
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
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 tested, 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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1022656217772
Permalink
