International Journal of Soft Computing

Year: 2006
Volume: 1
Issue: 1
Page No. 52 - 60

Hierarchical Reinforcement Learning with Grammar-Directed GA-P

Authors : Santi Garcia Carbajal and Nouhad J.rizk

Abstract: This article proposes a grammatical approach to hierarchical reinforcement learning.It is based on the grammatical description of a problem,a complex task,or objective.The use of a grammar to control the learning process,constraining the structure of the solutions generated with standard GP, permits the inclusion of knowledge about the problem in a straightforward manner,if this knowledge exists.When the problem to be solved involves the use of fuzzy concepts,the membership functions can be evolved simultaneously within the learning process using the advantages of the GA-P paradigm. Additionally,the inclusion of penalty factors in the evaluation function allows us to try to bias the search toward solutions that are optimal in safety or economical terms,not only taking into account control matters.We tested this approach with a real problem,obtaining three difierent control policies as a consequence of the different fitness functions employed.So,we conclude that the manipulation of fitness function and the use of a grammar to introduce as much knowledge as possible into the search process are useful tools when applying evolutionary techniques in industrial environments.The modified fitness functions and genetic operators are discussed in the paper,too.

How to cite this article:

Santi Garcia Carbajal and Nouhad J.rizk , 2006. Hierarchical Reinforcement Learning with Grammar-Directed GA-P. International Journal of Soft Computing, 1: 52-60.

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