Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents

TL;DR

This paper compares Q-learning and NEAT algorithms to train autonomous driving agents, aiming to identify which approach better learns to navigate on a given path for safer, more efficient self-driving vehicles.

Contribution

It provides a comparative analysis of Q-learning and NEAT for autonomous driving, highlighting their strengths and weaknesses in training self-driving agents.

Findings

Q-learning showed faster convergence in simple environments.

NEAT demonstrated better adaptability to complex driving scenarios.

Both algorithms improved driving performance compared to baseline methods.

Abstract

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.