Multi-Objective Multi-Agent Deep Reinforcement Learning for Dynamic Pricing and RideSharing Optimization

This paper presents an integrated framework for autonomous mobility-on-demand (AMoD) systems, focusing on dynamic pricing, ride-sharing, and decentralized coordination. Built on a high-fidelity, city-scale environment calibrated with NYC taxi data, the framework dynamically generates passenger and shared autonomous vehicle (SAV) agents based on realworld spatiotemporal demand patterns. The system integrates a multi-objective multi-agent deep reinforcement learning (MOMADRL) framework with centralized training and decentralized execution (CTDE), allowing agents to optimize individual incentives and system-level social welfare jointly. Adaptive pricing strategies, flexible ride-matching mechanisms, and zone-based geographic abstractions are included to enhance computational efficiency while maintaining geographic realism. Experimental results demonstrate that our framework consistently improves key performance indicators like passenger waiting times, vehicle utilization, and pricing stability, outperforming purely centralized or decentralized methods. This research provides a robust platform for testing adaptive policies and advancing scalable, equitable AMoD system design.