Reinforcement learning accelerated with artificial neural network for maze and search problems

Reinforcement learning is the problem faced by an agent that must learn behaviour through trial and error interactions with a dynamic environment that lacks the educational examples. Q-learning is one of the most popular algorithms among the reinforcement learning methods. Artificial neural network, as in reinforcement learning, is a sub-entry of machine learning, which can be applied on real frames, the environment of which we do not have sufficient information. Our aim is to enable an autonomous agent placed in a maze to find the shortest path to the target by combining q learning and artificial neural network.