# Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging

arXiv: https://arxiv.org/abs/2108.01466 · HTML: https://mlsanity.com/paper/2108.01466/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 83 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Risk Averse Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1904.00511/) (2019)
- [An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization](https://mlsanity.com/paper/1505.04824/) (2015)
- [Deep Learning Based Caching for Self-Driving Car in Multi-access Edge Computing](https://mlsanity.com/paper/1810.01548/) (2018)
- [Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning](https://mlsanity.com/paper/1809.10679/) (2018)
- [Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems](https://mlsanity.com/paper/2002.08567/) (2020)
- [Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems](https://mlsanity.com/paper/1712.04135/) (2017)
- [Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2003.02157/) (2020)
- [Smoothed Least-Laxity-First Algorithm for EV Charging](https://mlsanity.com/paper/2102.08610/) (2021)
- [Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations](https://mlsanity.com/paper/1708.07626/) (2017)
- [Peer Offloading with Delayed Feedback in Fog Networks](https://mlsanity.com/paper/2011.11835/) (2020)

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

- [Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid](https://mlsanity.com/paper/2306.07993/) (2023)

## Abstract (arXiv)

In this paper, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an EV charging facility for human-driven connected vehicles (CVs) and autonomous vehicles (AVs). The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and AVs. To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data-informed manner. We propose a novel risk adversarial multi-agent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. Experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE's active charging time and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.