Lithium-Ion (Li-I) batteries have recently become pervasive and are used in many physical assets. To enable a good prediction of the end of discharge of batteries, detailed electrochemical Li-I battery models have been developed. Their parameters are typically calibrated before they are taken into operation and are typically not re-calibrated during operation. However, since battery performance is affected by aging, the reality gap between the computational battery models and the real physical systems leads to inaccurate predictions. A supervised machine learning algorithm would require an extensive representative training dataset mapping the observation to the ground truth calibration parameters. This may be infeasible for many practical applications. In this paper, we implement a Reinforcement Learning-based framework for reliably and efficiently inferring calibration parameters of battery models. The framework enables real-time inference of the computational model parameters in order to compensate the reality-gap from the observations. Most importantly, the proposed methodology does not need any labeled data samples, (samples of observations and the ground truth calibration parameters). Furthermore, the framework does not require any information on the underlying physical model. The experimental results demonstrate that the proposed methodology is capable of inferring the model parameters with high accuracy and high robustness. While the achieved results are comparable to those obtained with supervised machine learning, they do not rely on the ground truth information during training.
Beyond Traditional DoE: Deep Reinforcement Learning for Optimizing Experiments in Model Identification of Battery Dynamics
Eatron Technologies Ltd.（伊创科技有限公司）；Istanbul Technical University Artificial Intelligence and Data Science Application and Research Center（伊斯坦布尔理工大学人工智能与数据科学应用研究中心）
Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning
Keele University（基尔大学）
随着世界寻求变得更加可持续，需要智能解决方案来提高可再生能源的渗透率。本文使用无模型深度强化学习算法Rainbow Deep Q-Networks来控制小型微电网中的电池，以执行能源套利并更有效地利用太阳能和风能。该电网根据基尔大学收集的数据集，利用自己的需求和可再生能源发电，并使用来自真实批发能源市场的动态能源定价。四个场景进行测试，包括使用需求和价格预测与当地天气数据。该算法及其子组件进行了评估，对两个连续的控制基准彩虹能够优于所有其他方法。这项研究表明了在复杂的环境和奖励函数中使用分布式方法进行强化学习的重要性，以及如何将其用于可视化和情境化现实世界应用中的代理行为。
tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
低功耗电子和机器学习技术的进步导致了许多新颖的可穿戴物联网设备。这些设备的电池容量和计算能力有限。因此，从环境源收集能量是为这些低能量可穿戴设备供电的有前途的解决方案。他们需要最佳地管理收集的能量，以实现能源中性操作，从而消除充电要求。由于目标设备的采集能量的动态性质和电池能量约束，最佳能量管理是一项具有挑战性的任务。为了应对这一挑战，我们提出了一个基于强化学习的能源管理框架tinyMAN，用于资源受限的可穿戴物联网设备。该框架在动态能量收集模式和电池约束下最大化目标设备的利用率。此外，tinyMAN不依赖于对收获能量的预测，这使得它成为一种无预测的方法。我们使用TensorFlow Lite for Micro在可穿戴设备原型上部署了tinyMAN，这要归功于其不到100 KB的小内存占用。我们的评估表明，tinyMAN实现小于2.36 ms和27.75 $μ$ J，同时保持高达45%的更高的效用相比，以前的方法。
On the effectiveness of reward functions in reinforcement learning for confidence calibration of large language models
School of Computing National University of Singapore（新加坡国立大学计算机学院）；Institute of Advanced Intelligence and Computing（高级智能与计算研究所）；Agency for Science, Technology and Research（科技研究局）；Department of Electrical & Computer Engineering（电气与计算机工程系）
Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China（中国科学院软件研究所信息处理实验室）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学网络安全学院）；National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China（中国国家计算机网络应急技术配合中心）
Joint Training of Multi-Token Prediction in Reinforcement Learning via Optimal Coefficient Calibration
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS）；Meituan（美团）
Ranking-Aware Calibration for Reliable Multimodal Reinforcement Learning
Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University, Beijing（计算机科学与技术系，人工智能研究院，BNRist中心，清华大学-博世联合机器学习中心，THBI实验室，清华大学，北京）；Dept. of Automation, Tsinghua University, Beijing（自动化系，清华大学，北京）
Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；China Mobile Communications Group Jilin Co., Ltd.（中国移动通信集团吉林有限公司）
Automatic re-calibration of quantum devices by reinforcement learning
Computer Vision Center (CVC)（计算机视觉中心）；Instituto de Física La Plata (IFLP)（拉普拉塔物理研究所）；CONICET（阿根廷科学与技术研究委员会）；Universidad Nacional de La Plata (UNLP)（拉普拉塔国立大学）；Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP)（拉普拉塔国立大学工程学院基础科学系）；Department of Computer Science, Universitat Autònoma de Barcelona (UAB)（巴塞罗那自治大学计算机科学系）
