Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Averaging Efficiently in the Presence of Noise
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Learning to Race: Experiments with a Simulated Race Car
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Neuroevolution of an automobile crash warning system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On-line evolutionary computation for reinforcement learning in stochastic domains
Proceedings of the 8th annual conference on Genetic and evolutionary computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Learning drivers for TORCS through imitation using supervised methods
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Controller for TORCS created by imitation
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
Learning, evolution and adaptation in racing games
Proceedings of the 9th conference on Computing Frontiers
Advanced overtaking behaviors for blocking opponents in racing games using a fuzzy architecture
Expert Systems with Applications: An International Journal
Car setup optimization via evolutionary algorithms
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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The application of on-line learning techniques to modern computer games is a promising research direction. In fact, they can be used to improve the game experience and to achieve a true adaptive game AI. So far, several works proved that neuroevolution techniques can be successfully applied to modern computer games but they are usually restricted to offline learning scenarios. In on-line learning problems the main challenge is to find a good trade-off between the exploration, i.e., the search for better solutions, and the exploitation of the best solution discovered so far. In this paper we propose an on-line neuroevolution approach to evolve non-player characters in The Open Car Racing Simulator (TORCS), a state-of-the-art open source car racing simulator. We tested our approach on two online learning problems: (i) on-line evolution of a fast controller from scratch and (ii) optimization of an existing controller for a new track. Our results show that on-line neuroevolution can effectively improve the performance achieved during the learning process.