Paper Info
Title
Applying Genetic Algorithms for the Improvement of an Autonomous Fuzzy Driver for Simulated Car Racing.
Abstract
Games offer a suitable testbed where new methodologies and algorithms can be tested in a near-real life environment. For example, in a car driving game, using transfer learning or other techniques results can be generalized to autonomous driving environments. In this work, we use evolutionary algorithms to optimize a fuzzy autonomous driver for the open simulated car racing game TORCS. The Genetic Algorithm applied improves the fuzzy systems to set an optimal target speed as well as the instantaneous steering angle during the race. Thus, the approach offer an automatic way to define the membership functions, instead of a manual or hill-climbing descent method. However, the main issue with this kind of algorithms is to define a proper fitness function that best delivers the obtained result, which is eventually to win as many races as possible. In this paper we define two different evaluation functions, and prove that fine-tuning the controller via evolutionary algorithms robustly finds good results and that, in many cases, they are able to play very competitively against other published results, with a more relying approach that needs very few parameters to tune. The optimized fuzzy-controllers (one per fitness) yield a very good performance, mainly in tracks that have many turning points, which are, in turn, the most difficult for any autonomous agent. Experimental results show that the enhanced controllers are very competitive with respect to the embedded TORCS drivers, and much more efficient in driving than the original fuzzy-controller.
Year
DOI
Venue
2018
10.1007/978-3-319-91479-4_20
Communications in Computer and Information Science
Keywords
Field
DocType
Videogames,Fuzzy controller,TORCS,Optimization,Genetic algorithms,Steering control
Control theory,Autonomous agent,Evolutionary algorithm,Computer science,Transfer of learning,Fuzzy logic,Fitness function,Artificial intelligence,Fuzzy control system,Genetic algorithm
Conference
Volume
ISSN
Citations 
855
1865-0929
0
PageRank 
References 
Authors
0.34
12
4
Name
Order
Citations
PageRank
Mohammed Salem131.43
Antonio Miguel Mora231442.81
Juan Julián Merelo Guervós348375.75
Pablo García-sánchez418232.32