Technical Note: \cal Q-Learning
Machine Learning
An overview of vehicle routing problems
The vehicle routing problem
GVR: A New Genetic Representation for the Vehicle Routing Problem
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
On the influence of GVR in vehicle routing
Proceedings of the 2003 ACM symposium on Applied computing
A genetic algorithm for unmanned aerial vehicle routing
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On the convergence of stochastic iterative dynamic programming algorithms
Neural Computation
Dynamic route planning for car navigation systems using virus genetic algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
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The development of information technologies realises an on-demand transport (pick-up) system. In this paper, we simulate transport situations for the system based on multi-agent model to find efficient strategies. We examine four types of driver agents; random agent, greedy agent, Q-learning agent, and Genetic agent. Random agent and Greedy agents select the next pick-up points from its surround without learning and optimisation. In contrast, Q-learning agent estimates the expectation value of pick-up quantity by Q-learning, and Genetic agent optimises its travel routes by Genetic algorithm. Finally, we report our experimental results to evaluate the effect of the four strategies.