AIJ

Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration

面向可持续电动汽车充电与二氧化碳减排的排放感知强化学习：在不同可再生能源渗透率下

Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon, Shafkat Khan Siam, Shafiuzzaman K Khadem, Krishnendu Guha, Mayeen U Khandaker, Md. Noor-A-Rahim

机构 * organization= nasc Research, School of Computer Science \& IT, University College Cork , country = IE ； organization= School of Computing, Engineering ； Digital Technologies, Teesside University , country= UK ； organization= International Energy Research Centre, Tyndall National Institute, Cork , country = IE ； Radiation Technologies Group, CCDCU, Faculty of Engineering ； Technology, Sunway University , country = Malaysia ； organization= Department of Physics, College of Science, Korea University , country = Republic of Korea 纠错

AI总结 提出基于软演员-评论家算法的排放感知强化学习策略，通过多目标奖励函数优化电动汽车充电调度，在EV2Gym平台上实现高达87%的碳排放减少和52%的可再生能源自消纳率。

Comments Submitted the Engineering Applications of Artificial Intelligence Journal (Elsevier)

An Offline Risk-aware Policy Selection Method for Bayesian Markov Decision Processes

贝叶斯马尔可夫决策过程的离线风险感知策略选择方法

Giorgio Angelotti, Nicolas Drougard, Caroline Ponzoni Carvalho Chanel

机构 * Natural Intelligence Toulouse Institute, University of Toulouse, France（图卢兹大学自然智能研究所） ； ISAE-SUPAERO, University of Toulouse, France（图卢兹大学ISAE-SUPAERO） 纠错

AI总结 针对离线强化学习中模型不确定性导致策略风险高的问题，提出一种基于贝叶斯形式化框架的风险感知策略选择方法EvC，通过最大化贝叶斯后验下的风险感知目标来选择稳健策略。

Comments Preprint, under review

Journal ref Artificial Intelligence, Volume 354, 2026