Comparative Analysis of Exploration Algorithms in Deep Reinforcement Learning for Autonomous Driving

Abstract

Autonomous driving is a rapidly evolving field that aims to enhance road safety and reduce accidents through the use of advanced software and hardware technologies. Reinforcement learning (RL) combined with deep neural networks has emerged as a promising approach for training autonomous agents. This research paper investigates three exploration algorithms —Epsilon-Greedy, Random Network Distillation (RND), and Bootstrapped Deep Q-Network (DQN)— within the context of autonomous driving. Performance is assessed based on episodic returns in training and testing environments, as well as the time required to train the networks. The results show significant improvement in learning capability using Bootstrapped DQN without critical differences in training time. There also exists a potential to increase episodic returns further given an increase in the number of steps to train the models.