Multi-objective Reinforcement Learning for Energy Harvesting Wireless Sensor Nodes
Author:
Affiliation:
1. Keio University
2. The University of Tokyo
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9691770/9691932/09691998.pdf?arnumber=9691998
Reference20 articles.
1. A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems;fei;IEEE Communications Surveys & Tutorials,2016
2. Multiobjective Reinforcement Learning for Cognitive Satellite Communications Using Deep Neural Network Ensembles
3. Reward Function Evaluation in a Reinforcement Learning Approach for Energy Management
4. A generalized algorithm for multi-objective reinforcement learning and policy adaptation;yang;Ad-vances in Neural Information Processing Systems,2019
5. Multi-objective reinforcement learning with continuous pareto frontier approximation;pirotta;Twenty-Ninth AAAI Conference on Artificial Intelligence,0
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1. Enhancing Deep Reinforcement Learning with Compressed Sensing-based State Estimation;2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC);2023-12-18
2. Multi-Objective Resource Scheduling for IoT Systems Using Reinforcement Learning;Journal of Low Power Electronics and Applications;2022-10-08
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