A learning classifier system for mazes with aliasing clones |
| |
Authors: | Zhanna V Zatuchna Anthony J Bagnall |
| |
Affiliation: | (1) School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, England |
| |
Abstract: | Maze problems represent a simplified virtual model of the real environment and can be used for developing core algorithms
of many real-world application related to the problem of navigation. Learning Classifier Systems (LCS) are the most widely
used class of algorithms for reinforcement learning in mazes. However, LCSs best achievements in maze problems are still mostly
bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons for failure.
Moreover, there is a lack of knowledge of what makes a maze problem hard to solve by a learning agent. To overcome this restriction
we try to improve our understanding of the nature and structure of maze environments. In this paper we describe a new LCS
agent that has a simpler and more transparent performance mechanism. We use the structure of a predictive LCS model, strip
out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability to Associative
Perception, adopted from psychology. We then assess the new LCS with Associative Perception on an extensive set of mazes and
analyse the results to discover which features of the environments play the most significant role in the learning process.
We identify a particularly hard feature for learning in mazes, aliasing clones, which arise when groups of aliasing cells
occur in similar patterns in different parts of the maze. We discuss the impact of aliasing clones and other types of aliasing
on learning algorithms. |
| |
Keywords: | Learning agents Learning Classifier Systems Associative perception Navigation Aliasing Maze |
本文献已被 SpringerLink 等数据库收录! |
|