How can I optimize ride-sharing rebalancing with fairness?
RIDE-SOURCING VEHICLE REBALANCING WITH SERVICE ACCESSIBILITY GUARANTEES VIA CONSTRAINED MEAN-FIELD REINFORCEMENT LEARNING
April 1, 2025
https://arxiv.org/pdf/2503.24183This paper tackles the problem of efficiently rebalancing ride-sourcing vehicles (like Uber/Lyft) while ensuring equitable service access across all areas, not just high-demand zones. It uses Mean-Field Reinforcement Learning (MFRL) and Mean-Field Control (MFC) to model and optimize the movement of a large fleet of vehicles. An accessibility constraint is integrated to guarantee minimum service levels in all areas. The complex vehicle-rider matching process is either approximated using optimal transport theory or learned through interaction with a realistic simulator.
Key points for LLM-based multi-agent systems:
- Scalability: Mean-field methods address the scalability challenge of traditional multi-agent RL by considering the interaction of a single agent with the aggregate behavior of the fleet. This is particularly relevant for large-scale LLM-based systems.
- Constraint integration: The demonstrated integration of service accessibility constraints into the learning framework showcases how LLMs can be trained to optimize for multiple objectives, including fairness and equity considerations.
- Simulation for learning: The use of a realistic simulator incorporating a matching module highlights the importance of simulated environments for training LLM-based agents in complex, dynamic scenarios, especially where real-world data is limited or expensive to obtain.
- Model-based vs. Model-free: The comparison of MFC (model-based) and MFRL (model-free) provides insights into the trade-offs between sample efficiency and model accuracy relevant for choosing the appropriate learning paradigm for LLM agents.