@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}, }