Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents
This addresses the problem of improving autonomous driving safety to reduce road traffic deaths, but it is incremental as it focuses on comparing existing methods.
The paper compares Q-learning and NeuroEvolution of Augmenting Topologies (NEAT) for training self-driving agents to navigate a path, aiming to identify which algorithm performs better in this context.
Autonomous driving vehicles have been of keen interest ever since automation of various tasks started. Humans are prone to exhaustion and have a slow response time on the road, and on top of that driving is already quite a dangerous task with around 1.35 million road traffic incident deaths each year. It is expected that autonomous driving can reduce the number of driving accidents around the world which is why this problem has been of keen interest for researchers. Currently, self-driving vehicles use different algorithms for various sub-problems in making the vehicle autonomous. We will focus reinforcement learning algorithms, more specifically Q-learning algorithms and NeuroEvolution of Augment Topologies (NEAT), a combination of evolutionary algorithms and artificial neural networks, to train a model agent to learn how to drive on a given path. This paper will focus on drawing a comparison between the two aforementioned algorithms.