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Reinforcement Learning for Power Management of Batteryless Sensing Systems.. University of California, San Diego, USA, (2020)Ember: energy management of batteryless event detection sensors with deep reinforcement learning., , , , and . SenSys, page 503-516. ACM, (2020)Marble: collaborative scheduling of batteryless sensors with meta reinforcement learning., , , and . BuildSys@SenSys, page 208-209. ACM, (2021)Scaling configuration of energy harvesting sensors with reinforcement learning., , and . ENSsys@SenSys, page 7-13. ACM, (2018)Opportunistic hierarchical classification for power optimization in wearable movement monitoring systems., , , , , and . SIES, page 102-111. IEEE, (2012)Resource-Efficient Computing in Wearable Systems., , , , and . SMARTCOMP, page 150-155. IEEE, (2019)Ember - energy management of batteryless event detection sensors with deep reinforcement learning: demo abstract., , , , and . SenSys, page 609-610. ACM, (2020)Demo Abstract: Pible: Battery-Free Mote for Perpetual Indoor BLE Applications., , , , and . CoRR, (2019)Pible: battery-free mote for perpetual indoor BLE applications., , , , and . BuildSys@SenSys, page 168-171. ACM, (2018)Quantifying the impact of variability on the energy efficiency for a next-generation ultra-green supercomputer., , , , and . ISLPED, page 295-298. ACM, (2014)