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Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation [article]

Haruka Kiyohara, Kosuke Kawakami, Yuta Saito
2021 arXiv   pre-print
In recommender systems (RecSys) and real-time bidding (RTB) for online advertisements, we often try to optimize sequential decision making using bandit and reinforcement learning (RL) techniques.  ...  In this position paper, we explore the potential of using simulation to accelerate practical research of offline RL and OPE, particularly in RecSys and RTB.  ...  INTRODUCTION In recommender systems (RecSys) and real-time bidding (RTB) for online advertisements, we often use sequential decision making algorithms to increase sales or to enhance user satisfaction.  ... 
arXiv:2109.08331v1 fatcat:uiwkdbubfbdkpojduaaijddwzy

Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review [article]

Ziqing Zhu, Ze Hu, Ka Wing Chan, Siqi Bu, Bin Zhou, Shiwei Xia
2022 arXiv   pre-print
Finally, some RL techniques that have great potentiality to be deployed in bidding and dispatching problems are recommended and discussed.  ...  For each application, apart from a paradigmatic summary of generalized methodology, in-depth discussions of applicability and obstacles while deploying RL techniques are also provided.  ...  Applications of Reinforcement Learning in Deregulated Power Market Operation Bidding and Pricing Strategy Optimization in Power market As mentioned in Section 1.1, the dynamic optimization of bidding  ... 
arXiv:2205.08369v1 fatcat:yqdarokpnzf4zitcilkgigq4vm

Learning Adaptive Display Exposure for Real-Time Advertising [article]

Weixun Wang, Junqi Jin, Jianye Hao, Chunjie Chen, Chuan Yu, Weinan Zhang, Jun Wang, Xiaotian Hao, Yixi Wang, Han Li, Jian Xu, Kun Gai
2019 arXiv   pre-print
Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight  ...  To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism.  ...  A.2 Related Work A.2.1 Bidding Optimization in Real-Time Bidding.  ... 
arXiv:1809.03149v2 fatcat:snbzuogfkrdhjctvm65ligo5yu

QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge [article]

Rajarshi Bhattacharyya, Archana Bura, Desik Rengarajan, Mason Rumuly, Bainan Xia, Srinivas Shakkottai, Dileep Kalathil, Ricky K. P. Mok, Amogh Dhamdhere
2020 arXiv   pre-print
However, current access networks treat all packets identically, and lack the agility to determine which clients are most in need of service at a given time.  ...  The goal of this work is to design, develop and demonstrate QFlow, a learning approach to create a value chain from the application on one side, to algorithms operating over reconfigurable infrastructure  ...  Third, we need to learn what is the relation between realized QoE and the configuration used (using a combination of offline and online learning).  ... 
arXiv:1901.00959v3 fatcat:cajprxovyfeetor3sh7k7hbxmu

Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI [article]

Le Xie, Xiangtian Zheng, Yannan Sun, Tong Huang, Tony Bruton
2022 arXiv   pre-print
This article presents a use-inspired perspective of the opportunities and challenges in a massively digitized power grid.  ...  in the power grid.  ...  ACKNOWLEDGEMENTS The authors sincerely thank Jimmy Liu, Steven Dennis, and Thomas Wilson for their help on the Oncor use cases presented in this paper.  ... 
arXiv:2205.05180v1 fatcat:ecmq2wqy2nhk7e2zcabwdkhltq

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking.  ...  One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications.  ...  Instead, a simulator that mimics the real network environments is built and the agent is trained offline using that.  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking.  ...  One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications.  ...  Instead, a simulator that mimics the real network environments is built and the agent is trained offline using that.  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

A Survey on Deep Reinforcement Learning for Data Processing and Analytics [article]

Qingpeng Cai, Can Cui, Yiyuan Xiong, Wei Wang, Zhongle Xie, Meihui Zhang
2022 arXiv   pre-print
Recently, reinforcement learning, deep reinforcement learning (DRL) in particular, is increasingly explored and exploited in many areas because it can learn better strategies in complicated environments  ...  Finally, we discuss important open challenges and future research directions of using DRL in data processing and analytics.  ...  We cover a large number of topics ranging from fundamental problems in system areas such as tuning and scheduling to important applications such as healthcare and fintech.  ... 
arXiv:2108.04526v3 fatcat:kcusgp7jzfbf7ov5os7gwf2e6i

A Metaverse: taxonomy, components, applications, and open challenges

Sang-Min Park, Young-Gab Kim
2022 IEEE Access  
Furthermore, we describe essential methods based on three components and techniques to Metaverse's representative Ready Player One, Roblox, and Facebook research in the domain of films, games, and studies  ...  Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is based on the social value of Generation Z that online and offline selves are not different.  ...  In terms of application, digital twins are used to solve current problems and simulate future outcomes.  ... 
doi:10.1109/access.2021.3140175 fatcat:fnraeaz74vh33knfvhzrynesli

Approximately Orchestrated Routing and Transportation Analyzer: Large-scale traffic simulation for autonomous vehicles

Dustin Carlino, Mike Depinet, Piyush Khandelwal, Peter Stone
2012 2012 15th International IEEE Conference on Intelligent Transportation Systems  
The second, externality pricing, learns the travel time of a variety of different routes and defines a localized notion of cost imposed on other drivers by following the route.  ...  AORTA is designed to provide reasonably realistic simulation of any city in the world with zero configuration, to run on cheap machines, and with an emphasis on easy use and simple code.  ...  A step would be scored using the dot product of this vector with a fixed weight vector. Different weights yield different paths. A variety of useful weight vectors could be learned offline.  ... 
doi:10.1109/itsc.2012.6338701 dblp:conf/itsc/CarlinoDKS12 fatcat:5jh6m7rsefh6rp4x3ccuqpq42i

Data-driven predictive control for unlocking building energy flexibility: A review

Anjukan Kathirgamanathan, Mattia De Rosa, Eleni Mangina, Donal P. Finn
2021 Renewable & Sustainable Energy Reviews  
As significant end-use consumers, and through better grid integration, buildings are expected to play an expanding role in the future smart grid.  ...  A R T I C L E I N F O Keywords: Review Building energy flexibility Data-driven Machine learning Model predictive control (MPC) Smart grid A B S T R A C T Managing supply and demand in the electricity grid  ...  In standard RL, the policy (based upon which the agent takes action) is updated online at every time step, however, Batch Reinforcement Learning (BLR) is a variation where the policy is calculated offline  ... 
doi:10.1016/j.rser.2020.110120 fatcat:mtgf5qb3ibghxmq4b6aoxlm56m

Data-driven Predictive Control for Unlocking Building Energy Flexibility: A Review [article]

Anjukan Kathirgamanathan, Mattia De Rosa, Eleni Mangina, Donal P. Finn
2020 arXiv   pre-print
As significant end-use consumers, and through better grid integration, buildings are expected to play an expanding role in the future smart grid.  ...  Managing supply and demand in the electricity grid is becoming more challenging due to the increasing penetration of variable renewable energy sources.  ...  In standard RL, the policy (based upon which the agent takes action) is updated online at every time step, however, Batch Reinforcement Learning (BLR) is a variation where the policy is calculated offline  ... 
arXiv:2007.14866v1 fatcat:w2um4o22nzebtgqxkbh2jcqe4u

Recent Advances in Reinforcement Learning in Finance [article]

Ben Hambly, Renyuan Xu, Huining Yang
2021 arXiv   pre-print
learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments.  ...  This survey paper aims to review the recent developments and use of RL approaches in finance.  ...  Acknowledgement We thank Anran Hu, Wenpin Tang, Zhuoran Yang, Junzi Zhang and Zeyu Zheng for helpful discussions and comments on this survey.  ... 
arXiv:2112.04553v1 fatcat:ay66scqcknhrlkvyvhlzonx4gy

2020 Index IEEE Transactions on Intelligent Transportation Systems Vol. 21

2020 IEEE transactions on intelligent transportation systems (Print)  
., +, TITS Aug. 2020 3447-3456 Real-Time Vehicle Make and Model Recognition Using Unsupervised Feature Learning.  ...  ., +, TITS Aug. 2020 3447-3456 Real-Time Vehicle Make and Model Recognition Using Unsupervised Fea- ture Learning.  ...  R Radar clutter Outliers-Robust CFAR Detector of Gaussian Clutter Based on the Truncated-Maximum-Likelihood-Estimator in SAR Imagery. Ai, J., 2039 -2049  ... 
doi:10.1109/tits.2020.3048827 fatcat:ab6he3jkfjboxg7wa6pagbggs4

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

Jiafu Wan, Xiaomin Li, Hong-Ning Dai, Andrew Kusiak, Miguel Martinez-Garcia, Di Li
2020 Proceedings of the IEEE  
The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed.  ...  Challenges and solutions related to AI in CM are also discussed.  ...  The data layer included a distributed database for real-time data storing, and the relational database was used to associate the real-time data.  ... 
doi:10.1109/jproc.2020.3034808 fatcat:bpljlzguqjhypedblczhmch2uq
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