# Press Start to Charge: Videogaming the Online Centralized Charging Scheduling Problem

arXiv: https://arxiv.org/abs/2601.12543 · HTML: https://mlsanity.com/paper/2601.12543/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 67 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks](https://mlsanity.com/paper/1703.02930/) (2017)
- [Combinatorial optimization and reasoning with graph neural networks](https://mlsanity.com/paper/2102.09544/) (2021)
- [Analysis of convolutional neural network image classifiers in a rotationally symmetric model](https://mlsanity.com/paper/2205.05500/) (2022)
- [Online Coordinated Charging Decision Algorithm for Electric Vehicles without Future Information](https://mlsanity.com/paper/1310.3580/) (2013)
- [Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks](https://mlsanity.com/paper/2301.06087/) (2023)
- [A Model Predictive Control Approach for Low-Complexity Electric Vehicle Charging Scheduling: Optimality and Scalability](https://mlsanity.com/paper/1502.01456/) (2015)
- [Online Charging Scheduling Algorithms of Electric Vehicles in Smart Grid: An Overview](https://mlsanity.com/paper/1609.02791/) (2016)
- [Complexity of Scheduling Charging in the Smart Grid](https://mlsanity.com/paper/1709.07480/) (2017)
- [Gamifying the Vehicle Routing Problem with Stochastic Requests](https://mlsanity.com/paper/1911.05922/) (2019)

## Abstract (arXiv)

We study the online centralized charging scheduling problem (OCCSP). In this problem, a central authority must decide, in real time, when to charge dynamically arriving electric vehicles (EVs), subject to capacity limits, with the objective of balancing load across a finite planning horizon. To solve the problem, we first gamify it; that is, we model it as a game where charging blocks are placed within temporal and capacity constraints on a grid. We design heuristic policies, train learning agents with expert demonstrations, and improve them using Dataset Aggregation (DAgger). From a theoretical standpoint, we show that gamification reduces model complexity and yields tighter generalization bounds than vector-based formulations. Experiments across multiple EV arrival patterns confirm that gamified learning enhances load balancing. In particular, the image-to-movement model trained with DAgger consistently outperforms heuristic baselines, vector-based approaches, and supervised learning agents, while also demonstrating robustness in sensitivity analyses. These operational gains translate into tangible economic value. In a real-world case study for the Greater Montr'eal Area (Qu'ebec, Canada) using utility cost data, the proposed methods lower system costs by tens of millions of dollars per year over the prevailing practice and show clear potential to delay costly grid upgrades.