# Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation

arXiv: https://arxiv.org/abs/2502.13392 · HTML: https://mlsanity.com/paper/2502.13392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 32 of its 92 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 30 of 32)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.11454/) (2019)
- [Reinforcement Learning for Molecular Design Guided by Quantum Mechanics](https://mlsanity.com/paper/2002.07717/) (2020)
- [A Deep Value-network Based Approach for Multi-Driver Order Dispatching](https://mlsanity.com/paper/2106.04493/) (2021)
- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem](https://mlsanity.com/paper/1911.11260/) (2019)
- [Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching](https://mlsanity.com/paper/1910.02591/) (2019)
- [CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms](https://mlsanity.com/paper/1905.11353/) (2019)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2212.07313/) (2022)
- [Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand](https://mlsanity.com/paper/2202.07147/) (2022)
- [Scalable Deep Reinforcement Learning for Ride-Hailing](https://mlsanity.com/paper/2009.14679/) (2020)
- [A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning](https://mlsanity.com/paper/1910.14002/) (2019)
- [Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning](https://mlsanity.com/paper/1905.09906/) (2019)
- [Approximate Dynamic Programming for Planning a Ride-Sharing System using Autonomous Fleets of Electric Vehicles](https://mlsanity.com/paper/1810.08124/) (2018)
- [A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem](https://mlsanity.com/paper/2105.13284/) (2021)
- [i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance](https://mlsanity.com/paper/2401.04429/) (2024)
- [Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting](https://mlsanity.com/paper/2107.00165/) (2021)
- [A Branch-and-Price Algorithm for the Electric Autonomous Dial-A-Ride Problem](https://mlsanity.com/paper/2206.13496/) (2022)
- [A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem](https://mlsanity.com/paper/2209.02921/) (2022)
- [Multi-Agent Soft Actor-Critic with Coordinated Loss for Autonomous Mobility-on-Demand Fleet Control](https://mlsanity.com/paper/2404.06975/) (2024)
- [Real Time Operation of High-Capacity Electric Vehicle Ridesharing Fleets](https://mlsanity.com/paper/2101.01274/) (2021)
- [Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning](https://mlsanity.com/paper/2302.14833/) (2023)
- [Electric Vehicle Fleet and Charging Infrastructure Planning](https://mlsanity.com/paper/2306.10178/) (2023)
- [Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach](https://mlsanity.com/paper/2307.06742/) (2023)
- [Deep reinforcement learning for the dynamic vehicle dispatching problem: An event-based approach](https://mlsanity.com/paper/2307.07508/) (2023)
- [Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times](https://mlsanity.com/paper/2208.14852/) (2022)

## Built on by (most cited first, 1 of 1)

- [OpenHail: An Event-Driven Gymnasium Environment for Electric Ride-Hailing Fleet Control](https://mlsanity.com/paper/2609.30628/) (2026)

## Abstract (arXiv)

Pioneering companies such as Waymo have deployed electric robo-taxi services in several U.S. cities. Operating such fleets requires coordinating ride matching, vehicle repositioning, and battery charging in a stochastic environment while accounting for limited charging infrastructure. Understanding both how to design efficient, scalable control algorithms for large fleets and how charger deployment shapes system performance is therefore critical for robo-taxi operators. We model the operations of an electric robo-taxi system as a discrete-time Markov decision process with a long-run average reward objective. To efficiently evaluate system performance under different configurations, we develop a scalable deep reinforcement learning algorithm, Atomic Proximal Policy Optimization (Atomic-PPO), which reduces the action space using atomic action decomposition. The resulting training procedure requires only modest computational resources, making it practical to evaluate a large number of system configurations. We calibrate a simulation environment using real-world NYC for-hire vehicle trip records and benchmark Atomic-PPO against heuristic policies and a fluid-based reward upper bound. The results show that Atomic-PPO produces near-optimal fleet dispatching policies and serves as a reliable tool for system evaluation. We conduct extensive numerical experiments to evaluate how charging infrastructure and fleet size affect system performance. Our findings highlight the importance of ridership-based charger placement, balanced investments in charger quantity and charging speed, and joint planning of charging infrastructure and fleet size.