Investigations on Improvised Neural Network
Chess Engine for Augmenting Topologies

M Suresh Babu; E. Keshava Reddy

doi:https://doi.org/10.14445/22315373/IJMTT-V48P530

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 48 | Number 3 | Year 2017 | Article Id. IJMTT-V48P530 | DOI : https://doi.org/10.14445/22315373/IJMTT-V48P530

Investigations on Improvised Neural Network Chess Engine for Augmenting Topologies

M Suresh Babu, E. Keshava Reddy

Abstract

In this paper, reinforcement learning in a chess engine with the help of genetic algorithm was investigated. Thereinforcement learning methodology is used in the chess engine to get trained in to play games. The engine initially starts with null information about the game, however, every further moves in the game gets stored within the chess engine. Hencechess engine gains experience to discharge best performance with continuing games. The engine uses temporal difference leaf learning approach to improve the skill set in every level. The engine performs a brute force search of all possible positions that result from the given position on the board. The positions are evaluated using an evaluation function. The evaluation function incorporated with present engine is a neural network which returns a value for each position indicating its potential for either black or white. The neural network learns the function whose values are provided by the temporal difference leaf learning algorithm.

Keywords

Reinforcement Learning, Gene Chess Engine, NEAT, Neural Network.

References

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Citation :

M Suresh Babu, E. Keshava Reddy, "Investigations on Improvised Neural Network Chess Engine for Augmenting Topologies," International Journal of Mathematics Trends and Technology (IJMTT), vol. 48, no. 3, pp. 214-217, 2017. Crossref, https://doi.org/10.14445/22315373/IJMTT-V48P530