Constrained Reinforcement Learning for Stochastic Dynamic Optimal Power Flow Control
Author:
Affiliation:
1. Cornell Tech, Cornell University,Department of Electrical and Computer Engineering,USA,10044
2. Lawrence Berkeley National Laboratory
Funder
National Science Foundation
U.S. Department of Energy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10252165/10252088/10253087.pdf?arnumber=10253087
Reference20 articles.
1. Natural policy gradient primal-dual method for constrained markov decision processes;ding;NeurIPS,2020
2. Convergence and sample complexity of natural policy gradient primal-dual methods for constrained mdps;ding;arXiv preprint arXiv 2206 02346,2022
3. Upper confidence primal-dual reinforcement learning for cmdp with adversarial loss;qiu;NeurIPS,2020
4. Learning Safe Policies via Primal-Dual Methods
5. Deep Reinforcement Learning with Double Q-Learning
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1. Network-Constrained Reinforcement Learning for Optimal EV Charging Control;2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm);2023-10-31
2. Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow;IEEE Transactions on Power Systems;2023
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