# Out-of-Distribution-Aware Electric Vehicle Charging

arXiv: https://arxiv.org/abs/2311.05941 · HTML: https://mlsanity.com/paper/2311.05941/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 51 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 29 of 29)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Temporal Difference Learning for Model Predictive Control](https://mlsanity.com/paper/2203.04955/) (2022)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Competitive caching with machine learned advice](https://mlsanity.com/paper/1802.05399/) (2018)
- [Learning-based Model Predictive Control for Safe Exploration](https://mlsanity.com/paper/1803.08287/) (2018)
- [The Primal-Dual method for Learning Augmented Algorithms](https://mlsanity.com/paper/2010.11632/) (2020)
- [Online metric algorithms with untrusted predictions](https://mlsanity.com/paper/2003.02144/) (2020)
- [On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law](https://mlsanity.com/paper/2005.09241/) (2020)
- [Near-Optimal Bounds for Online Caching with Machine Learned Advice](https://mlsanity.com/paper/1910.12172/) (2019)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework](https://mlsanity.com/paper/1609.01387/) (2016)
- [Control Regularization for Reduced Variance Reinforcement Learning](https://mlsanity.com/paper/1905.05380/) (2019)
- [Provably Efficient Model-Free Constrained RL with Linear Function Approximation](https://mlsanity.com/paper/2206.11889/) (2022)
- [Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems](https://mlsanity.com/paper/2106.10497/) (2021)
- [On the Regret Analysis of Online LQR Control with Predictions](https://mlsanity.com/paper/2102.01309/) (2021)
- [Online Algorithms with Multiple Predictions](https://mlsanity.com/paper/2205.03921/) (2022)
- [Parsimonious Learning-Augmented Caching](https://mlsanity.com/paper/2202.04262/) (2022)
- [Chasing Convex Bodies and Functions with Black-Box Advice](https://mlsanity.com/paper/2206.11780/) (2022)
- [Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot](https://mlsanity.com/paper/2003.03200/) (2020)
- [Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity](https://mlsanity.com/paper/2210.12312/) (2022)
- [Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions](https://mlsanity.com/paper/2106.09659/) (2021)
- [Can Q-Learning be Improved with Advice?](https://mlsanity.com/paper/2110.13052/) (2021)
- [Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach](https://mlsanity.com/paper/2107.07772/) (2021)
- [Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging](https://mlsanity.com/paper/2012.02636/) (2020)
- [Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions](https://mlsanity.com/paper/2307.10524/) (2023)
- [Learning-Based Predictive Control via Real-Time Aggregate Flexibility](https://mlsanity.com/paper/2012.11261/) (2020)
- [Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary](https://mlsanity.com/paper/2302.00985/) (2023)
- [Off-Policy Interval Estimation with Lipschitz Value Iteration](https://mlsanity.com/paper/2010.15392/) (2020)

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

- [Safe Exploitative Play with Untrusted Type Beliefs](https://mlsanity.com/paper/2411.07679/) (2024)

## Abstract (arXiv)

We tackle the challenge of learning to charge Electric Vehicles (EVs) with Out-of-Distribution (OOD) data. Traditional scheduling algorithms typically fail to balance near-optimal average performance with worst-case guarantees, particularly with OOD data. Model Predictive Control (MPC) is often too conservative and data-independent, whereas Reinforcement Learning (RL) tends to be overly aggressive and fully trusts the data, hindering their ability to consistently achieve the best-of-both-worlds. To bridge this gap, we introduce a novel OOD-aware scheduling algorithm, denoted OOD-Charging. This algorithm employs a dynamic "awareness radius", which updates in real-time based on the Temporal Difference (TD)-error that reflects the severity of OOD. The OOD-Charging algorithm allows for a more effective balance between consistency and robustness in EV charging schedules, thereby significantly enhancing adaptability and efficiency in real-world charging environments. Our results demonstrate that this approach improves the scheduling reward reliably under real OOD scenarios with remarkable shifts of EV charging behaviors caused by COVID-19 in the Caltech ACN-Data.