Paper Info
Title
Approximating Arbitrary Reinforcement Signal by Learning Classifier Systems using Micro Genetic Algorithm
Abstract
Learning Classifier Systems are Evolutionary Learning mechanisms which combine Genetic Algorithm and the Reinforcement Learning paradigm. Learning Classifier Systems try to evolve state-action-reward mappings to propose the best action for each environmental state to maximize the achieved reward. In the first versions of learning classifier systems, state-action pairs can only be mapped to a constant real-valued reward. So to model a fairly complex environment, LCSs had to develop redundant state-action pairs which had to be mapped to different reward values. But an extension to a well-known LCS, called Accuracy Based Learning Classifier System or XCS, was recently developed which was able to map state-action pairs to a linear reward function. This new extension, called XCSF, can develop a more compact population than the original XCS. But some further researches have shown that this new extension is not able to develop proper mappings when the input parameters are from certain intervals. As a solution to this issue, in our previous works, we proposed a novel solution inspired by the idea of using evolutionary approach to approximate the reward landscape. The first results seem promising, but our approach, called XCSFG, converged to the goal very slowly. In this paper, we propose a new extension to XCSFG which employs micro-GA which its needed population is extremely smaller than simple GA. So we expect micro-GA to help XCSFG to converge faster. Reported results show that this new extension can be assumed as an alternative approach in XCSF family with respect to its convergence speed, approximation accuracy and population compactness.
Year
Venue
Keywords
2008
Fundam. Inform.
micro genetic algorithm,learning classifier system,reward landscape,reinforcement learning paradigm,state-action pair,learning classifier systems,new extension,constant real-valued reward,different reward value,evolutionary learning mechanism,linear reward function,approximating arbitrary reinforcement signal,genetic algorithm,function approximation
Field
DocType
Volume
Convergence (routing),Population,Function approximation,Artificial intelligence,Classifier (linguistics),Margin classifier,Genetic algorithm,Machine learning,Mathematics,Reinforcement learning,Learning classifier system
Journal
86
Issue
ISSN
Citations 
1
0169-2968
0
PageRank 
References 
Authors
0.34
12
2
Name
Order
Citations
PageRank
Ali Hamzeh121429.47
Adel Torkaman Rahmani213919.77