Decentralized Machine Learning for Autonomous Vehicle Control

Abstract

This paper investigates the application of decentralized machine learning (DML) to autonomous vehicle control. Traditional centralized approaches to autonomous driving often face challenges related to scalability, communication bottlenecks, and vulnerability to single points of failure. This research proposes a novel system utilizing distributed reinforcement learning (DRL) across a network of autonomous vehicles. The core claim is that a decentralized system, enabling collaborative learning and adaptation, can achieve improved performance and robustness compared to centralized methods. The system employs vehicles to share observations and jointly learn navigation policies. The key mechanisms involve the distributed training of policies using techniques such as asynchronous difference methods and federated learning. The system addresses the problem of non-stationarity inherent in multi-agent systems by employing adaptive learning rates and robust policy aggregation strategies. We discuss the theoretical framework, highlighting key concepts like policy synchronization, convergence analysis, and the impact of communication constraints. Furthermore, we present a conceptual model of the system, outlining the interactions between vehicles and the learning process. The potential benefits of DML for autonomous vehicle control, including enhanced safety, increased scalability, and improved adaptability to dynamic environments, are explored. The research contributes to the growing field of DML by providing a foundational design and theoretical underpinnings for a robust and scalable autonomous driving system.