# Few-Shot Learning for Dynamic Operations of Automated Electric Taxi Fleets under Evolving Charging Infrastructure: A Meta-Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2601.21312 · HTML: https://mlsanity.com/paper/2601.21312/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## 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, 10 of 10)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
- [Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments](https://mlsanity.com/paper/1710.03641/) (2017)
- [Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks](https://mlsanity.com/paper/2004.14404/) (2020)
- [Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand](https://mlsanity.com/paper/2202.07147/) (2022)
- [ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2111.08067/) (2021)
- [One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms](https://mlsanity.com/paper/2507.15351/) (2025)
- [A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing [technical report]](https://mlsanity.com/paper/2107.08662/) (2021)
- [Designing an Optimized Electric Vehicle Charging Station Infrastructure for Urban Area: A Case study from Indonesia](https://mlsanity.com/paper/2209.03448/) (2022)

## Abstract (arXiv)

With the rapid expansion of electric vehicles (EVs) and charging infrastructure, the effective management of Autonomous Electric Taxi (AET) fleets faces a critical challenge in environments with dynamic and uncertain charging availability. While most existing research assumes a static charging network, this simplification creates a significant gap between theoretical models and real-world operations. To bridge this gap, we propose GAT-PEARL, a novel meta-reinforcement learning framework that learns an adaptive operational policy. Our approach integrates a graph attention network (GAT) to effectively extract robust spatial representations under infrastructure layouts and model the complex spatiotemporal relationships of the urban environment, and employs probabilistic embeddings for actor-critic reinforcement learning (PEARL) to enable rapid, inference-based adaptation to changes in charging network layouts without retraining. Through extensive simulations on real-world data in Chengdu, China, we demonstrate that GAT-PEARL significantly outperforms conventional reinforcement learning baselines, showing superior generalization to unseen infrastructure layouts and achieving higher overall operational efficiency in dynamic settings.