Exploring the Potential of Deep Reinforcement Learning for Autonomous Navigation in Complex Environments

Abstract

One of the most challenging problems in robotics and autonomous vehicles is autonomous navigation in complex and dynamic environments. Deep Reinforcement Learning (DRL), which enables agents to learn complicated behaviors autonomously through trial and error, has demonstrated that it has the potential to be an effective solution to this problem. By utilizing the Waymo open dataset and the Proximal Policy Optimisation (PPO) algorithm, this research paper aims to investigate the potential of DRL for autonomous navigation in complex environments. In the first step of this process, we conduct a literature review that focuses on numerous research that has studied the application of DRL for autonomous navigation in various settings. After that, we discuss our methodology, which entails utilizing PPO to instruct an agent navigating the Waymo dataset. According to the findings of our study, the trained agent can properly navigate through the environment, even when barriers and other dynamic elements are present. In addition, we assess our agent's performance using various criteria, such as the percentage of successful attempts, efficiency, and risk. According to our research's conclusions, DRL-based navigation systems have the potential to create genuinely autonomous systems that can navigate across surroundings that are both complicated and dynamic. In general, the findings of this study demonstrate how important it is to investigate the possibilities of DRL to find solutions to complex problems in the fields of robotics and autonomous cars.