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P-MARL: Prediction-Based Multi-Agent Reinforcement Learning for Non-Stationary Environments., , , , and . AAMAS, page 1897-1898. ACM, (2015)An Identity Privacy Preserving Incentivization Scheme for Participatory Sensing., , and . ICMU, page 1-6. IEEE, (2018)Demand-responsive rebalancing zone generation for reinforcement learning-based on-demand mobility., , , and . AI Commun., 34 (1): 73-88 (2021)Shared Autonomous Mobility on Demand: A Learning-Based Approach and Its Performance in the Presence of Traffic Congestion., , , and . IEEE Intell. Transp. Syst. Mag., 12 (4): 208-218 (2020)Energy-aware placement optimization of UAV base stations via decentralized multi-agent Q-learning., , and . CoRR, (2021)Variational Policy Chaining for Lifelong Reinforcement Learning., , and . ICTAI, page 1546-1550. IEEE, (2019)Adaptation to Unknown Situations as the Holy Grail of Learning-Based Self-Adaptive Systems: Research Directions., and . SEAMS@ICSE, page 252-253. IEEE, (2021)Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning., , , and . GLOBECOM, page 1-6. IEEE, (2021)Iterative Reward Shaping Using Human Feedback for Correcting Reward Misspecification., , , , , and . ECAI, volume 372 of Frontiers in Artificial Intelligence and Applications, page 788-794. IOS Press, (2023)Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods., , , , and . SE4SG@ICSE, page 25-32. IEEE Computer Society, (2013)