# Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2307.16228 · HTML: https://mlsanity.com/paper/2307.16228/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 30 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Robust Deep Reinforcement Learning with Adversarial Attacks](https://mlsanity.com/paper/1712.03632/) (2017)
- [Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem](https://mlsanity.com/paper/1911.11260/) (2019)
- [Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2104.11434/) (2021)
- [Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/1709.07032/) (2017)
- [What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?](https://mlsanity.com/paper/2212.02705/) (2022)
- [Model Predictive Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/1509.03985/) (2015)
- [Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networks](https://mlsanity.com/paper/1812.07499/) (2018)
- [Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties](https://mlsanity.com/paper/2210.10887/) (2022)
- [Data-Driven Robust Taxi Dispatch under Demand Uncertainties](https://mlsanity.com/paper/1603.06263/) (2016)
- [Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems under Demand and Supply Uncertainties](https://mlsanity.com/paper/2211.13797/) (2022)
- [Predictive Positioning and Quality Of Service Ridesharing for Campus Mobility On Demand Systems](https://mlsanity.com/paper/1609.08116/) (2016)
- [Dynamic vehicle redistribution and online price incentives in shared mobility systems](https://mlsanity.com/paper/1304.3949/) (2013)
- [A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/1607.04357/) (2016)
- [On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms](https://mlsanity.com/paper/1709.04906/) (2017)

## Built on by (most cited first, 6 of 6)

- [Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey](https://mlsanity.com/paper/2406.05392/) (2024)
- [Solving a Class of Non-Convex Minimax Optimization in Federated Learning](https://mlsanity.com/paper/2310.03613/) (2023)
- [RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics](https://mlsanity.com/paper/2308.15368/) (2023)
- [R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics](https://mlsanity.com/paper/2308.15039/) (2023)
- [Bridging Policy and Real-World Dynamics: LLM-Augmented Rebalancing for Shared Micromobility Systems](https://mlsanity.com/paper/2603.00176/) (2026)
- [TetraRL: A Self-Adaptive Runtime for On-Device Deep Reinforcement Learning Systems](https://mlsanity.com/paper/2606.13891/) (2026)

## Abstract (arXiv)

Electric autonomous vehicles (EAVs) are getting attention in future autonomous mobility-on-demand (AMoD) systems due to their economic and societal benefits. However, EAVs' unique charging patterns (long charging time, high charging frequency, unpredictable charging behaviors, etc.) make it challenging to accurately predict the EAVs supply in E-AMoD systems. Furthermore, the mobility demand's prediction uncertainty makes it an urgent and challenging task to design an integrated vehicle balancing solution under supply and demand uncertainties. Despite the success of reinforcement learning-based E-AMoD balancing algorithms, state uncertainties under the EV supply or mobility demand remain unexplored. In this work, we design a multi-agent reinforcement learning (MARL)-based framework for EAVs balancing in E-AMoD systems, with adversarial agents to model both the EAVs supply and mobility demand uncertainties that may undermine the vehicle balancing solutions. We then propose a robust E-AMoD Balancing MARL (REBAMA) algorithm to train a robust EAVs balancing policy to balance both the supply-demand ratio and charging utilization rate across the whole city. Experiments show that our proposed robust method performs better compared with a non-robust MARL method that does not consider state uncertainties; it improves the reward, charging utilization fairness, and supply-demand fairness by 19.28%, 28.18%, and 3.97%, respectively. Compared with a robust optimization-based method, the proposed MARL algorithm can improve the reward, charging utilization fairness, and supply-demand fairness by 8.21%, 8.29%, and 9.42%, respectively.