Multi-agent Awareness, No-Retracing, Diagonal Movement for Q-Learning

Considering the lack of development for faster and more efficient solutions to the Grid World Q-Learning problems, it is no surprise that the performance of Q-Learning for Grid Worlds tends to exponentially reduce in effectiveness, as board size increases. This is a problem, because Q-Learning could (conceivably) solve analytic and mathematical problems, if runtime performance were enhanced. The motivation for this research, therefore, is to increase the runtime performance of Q-Learning for Grid Worlds by development of Multiple Awareness No-Retracing Agents for Q-Learning (or MANAQL). An account of conventional Q-Learning for Grid World environments is given and includes the anatomy of a Q-Learning class, its functions and their intended interactions; A detailed account of the methodology used to create MANAQL and how its functions and components differ from those of conventional Q-Learning is also given. In the Literature Review, several other Q-Learning algorithms that focus on multiple agents are narratively assessed and the question of why so little work has been done on improving Q-Learning for Grid World is asked and answered. The time complexity of the centred and non-centred conventional Q-Learning algorithms are evaluated and compared with each other and with those of the centred and non-centred MANAQL algorithms. Applications of the new algorithm will be the focus of later work.