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全球能源互联网, 2023 能源革命和数字革命方兴未艾，正共同推动中国能源电力系统向新型电力系统转型升级。人工智能有助于新型电力系统实现精准建模、高效分析及智能决策控制，是新型电力系统构建的关键支撑技术。通过对人工智能在电力系统源、网、荷、储等关键环节的预测、建模、分析、优化控制等核心应用的现状进行综述，对元学习、无监督预训练、可解释性与人机混合增强等人工智能领域的技术发展和其在新型电力系统的应用进行分析展望，为中国人工智能技术与新型电力系统的深度融合发展提供参考借鉴。
赵日晓*, 闫冬, 周翔, 王新迎
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GenFedRL: a general federated reinforcement learning framework for deep reinforcement learning agents [PDF]
, 2023 To solve the problem that intelligent devices equipped with deep reinforcement learning agents lack effective security data sharing mechanisms in the intelligent Internet of things, a general federated reinforcement learning (GenFedRL) framework was ...
Biao JIN +4 more
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Adaptive pilot design for OFDM based on deep reinforcement learning [PDF]
, 2023 For orthogonal frequency division multiplexing (OFDM) systems, an adaptive pilot design algorithm based on deep reinforcement learning was proposed.The pilot design problem was formulated as a Markov decision process, where the index of pilot positions ...
Qiaoshou LIU +3 more
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大数据, 2022 推荐系统是解决社会媒体信息过载问题的重要手段。为了解决传统推荐系统无法优化用户长期体验的问题，研究人员提出了交互式推荐系统，并尝试使用深度强化学习优化推荐策略。但是，强化推荐算法面临反馈稀疏、从零学习影响用户体验、物品空间大等问题。为了解决上述问题，提出一种改进的知识增强策略引导的交互式强化推荐模型KGP-DQN。该模型构建行为知识图谱表示模块，将用户历史行为和知识图谱结合，解决反馈稀疏问题；构建策略初始化模块，根据用户历史行为为强化推荐系统提供初始化策略，解决从零学习影响用户体验的问题 ...
张宇奇, 黄晓雯, 桑基韬
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Maritime mobile edge computing offloading method based on deep reinforcement learning [PDF]
, 2022 The strong heterogeneity among the network nodes of the maritime information system brings complex and high-dimensional constraints for optimizing task offloading of the maritime mobile edge computing.The complex and diverse maritime applications also ...
Leilei MENG +3 more
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全球能源互联网, 2023 微网在分布式新能源消纳、负荷优化、提高能源利用效率等方面具有重要作用。但新能源出力的间歇性、负荷侧用电行为的随机性导致微网成为一个动态的复杂系统，难以通过准确的物理模型刻画，给微网优化运行带来巨大挑战。深度强化学习（deep reinforcement learning，DRL）通过与环境交互试错寻找最优策略，不依赖于新能源出力和负荷的精确建模，适用于解决序贯决策问题，在求解含有大量不确定性的微网优化运行难题时具有优势。为此，从DRL原理、DRL在单个微网以及微网群优化运行中的应用进行了综述与分析 ...
周翔, 王继业, 陈盛, 王新迎
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Survey on reinforcement learning based adaptive bit rate algorithm for mobile video streaming services [PDF]
, 2021 In recent years, with the continuous release of HTTP adaptive streaming (HAS) video datasets and network trace datasets, the machine learning methods, such as deep learning and reinforcement learning, have been continuously applied to adaptive bit rate ...
Jiafeng LI +4 more
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Forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning [PDF]
, 2022 The software defined network separates the control plane from the data plane to achieve flexible traffic scheduling, which can use network resources more efficiently.However, with the increase of the number of flow entries, load rate, the number of ...
Chuang SUN +4 more
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智慧农业, 2023 目的/意义人工智能（Artificial Intelligence，AI）技术已在学术和工程应用领域掀起了研究高潮，在地球物理参数和农业气象遥感参数反演方面也表现出了强大的应用潜力。目前大部分AI技术在地学和农学的应用还是“黑箱”，没有物理意义或缺乏可解释性及通用性。为了促进AI在地学和农学的应用和培养交叉学科的人才，本研究提出基于AI耦合物理和统计方法的地球物理参数反演范式理论。方法首先基于物理能量平衡方程进行物理逻辑推理，从理论上构造反演方程组，然后基于物理推导构建泛化的统计方法 ...
MAO Kebiao +15 more
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Research progress of deep reinforcement learning applied to text generation [PDF]
, 2020 With the recent exciting achievements of Google’s artificial intelligence system in the game of Go, deep reinforcement learning (DRL) has witnessed considerable development.
Cong XU +4 more
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