@article{(Open Science Index):https://publications.waset.org/pdf/10011980,
	  title     = {Simulation of Obstacle Avoidance for Multiple Autonomous Vehicles in a Dynamic Environment Using Q-Learning},
	  author    = {Andreas D. Jansson},
	  country	= {},
	  institution	= {},
	  abstract     = {The availability of inexpensive, yet competent hardware allows for increased level of automation and self-optimization in the context of Industry 4.0. However, such agents require high quality information about their surroundings along with a robust strategy for collision avoidance, as they may cause expensive damage to equipment or other agents otherwise. Manually defining a strategy to cover all possibilities is both time-consuming and counter-productive given the capabilities of modern hardware. This paper explores the idea of a model-free self-optimizing obstacle avoidance strategy for multiple autonomous agents in a simulated dynamic environment using the Q-learning algorithm.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {15},
	  number    = {4},
	  year      = {2021},
	  pages     = {267 - 272},
	  ee        = {https://publications.waset.org/pdf/10011980},
	  url   	= {https://publications.waset.org/vol/172},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 172, 2021},