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Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space., , , and . CoRR, (2020)A hybrid approach to very small scale electrical demand forecasting., , , , and . ISGT, page 1-5. IEEE, (2014)Scalable Multi-Agent Learning for Situationally-Aware Multiple-Access and Grant-Free Transmissions., , , , and . SPAWC, page 1-5. IEEE, (2019)P-MARL: Prediction-Based Multi-Agent Reinforcement Learning for Non-Stationary Environments., , , , and . AAMAS, page 1897-1898. ACM, (2015)A Multi-Agent Neural Network for Dynamic Frequency Reuse in LTE Networks., , and . ICC Workshops, page 1-6. IEEE, (2018)Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods., , , , and . SE4SG@ICSE, page 25-32. IEEE Computer Society, (2013)Using Deep Q-Learning to Prolong the Lifetime of Correlated Internet of Things Devices., , , and . ICC Workshops, page 1-6. IEEE, (2019)Prediction-Based Multi-Agent Reinforcement Learning for Inherently Non-Stationary Environments. Trinity College Dublin, Ireland, (2016)A dynamic forecasting method for small scale residential electrical demand., , , , and . IJCNN, page 3767-3774. IEEE, (2014)System Level Evaluation and Validation of the ns-3 LTE Module in 3GPP Reference Scenarios., , and . Q2SWinet@MSWiM, page 59-64. ACM, (2017)