A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area d...A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area decentralized collaborative framework,the controllable variables in each region are divided into several optimization layers,which is an effective method for solving the limitations posed by dimensionality.The HCEQ provides constant information on the interaction between the state-action value function matrices,as well as on the cooperative game equilibrium among agents in each region.After acquiring the optimal value function matrix in the pre-learning process,HCEQ is able to quickly achieve an optimal solution online.Simulation of the IEEE 57-bus system is performed,which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization,with the desired global search capabilities and convergence speed.展开更多
In adaptive channel allocation for secondary user （SU） of cognitive radio （CR） system, it is necessary to consider allocation process from the temporal perspective. In this article, a chain store game is modeled t...In adaptive channel allocation for secondary user （SU） of cognitive radio （CR） system, it is necessary to consider allocation process from the temporal perspective. In this article, a chain store game is modeled to achieve SU＇s equilibrium state. Due to the computational complexity of solving equilibrium states, the authors explore the correlated equilibrium （CE） by importing signal mechanisms based on time and sequence number. Also, correlated equilibrium based game algorithms are presented. Simulations show that these algorithms are superior to other allocation algorithms both in channel utilization and communication time.展开更多
The importance of transaction fees in maintaining blockchain security and sustainability has been confirmed by extensive research,although they are not mandatory in most current blockchain systems.To enhance blockchai...The importance of transaction fees in maintaining blockchain security and sustainability has been confirmed by extensive research,although they are not mandatory in most current blockchain systems.To enhance blockchain in the long term,it is crucial to design effective transaction pricing mechanisms.Different from the existing schemes based on auctions with more consideration about the profit of miners,we resort to game theory and propose a correlated equilibrium based transaction pricing mechanism through solving a pricing game among users with transactions,which can achieve both the individual and global optimum.To avoid the computational complexity exponentially increasing with the number of transactions,we further improve the game-theoretic solution with an approximate algorithm,which can derive almost the same results as the original one but costs significantly reduced time.We also propose a truthful assessment model for pricing mechanism to collect the feedback of users regarding the price suggestion.Extensive experimental results demonstrate the effectiveness and efficiency of our proposed mechanism.展开更多
基金supported in part by National Key Basic Research Program of China(973 Program:2013CB228205)National Natural Science Foundation of China(51177051,51477055).
文摘A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area decentralized collaborative framework,the controllable variables in each region are divided into several optimization layers,which is an effective method for solving the limitations posed by dimensionality.The HCEQ provides constant information on the interaction between the state-action value function matrices,as well as on the cooperative game equilibrium among agents in each region.After acquiring the optimal value function matrix in the pre-learning process,HCEQ is able to quickly achieve an optimal solution online.Simulation of the IEEE 57-bus system is performed,which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization,with the desired global search capabilities and convergence speed.
基金supported by the National Natural Science Foundation of China (60602019/F010106)
文摘In adaptive channel allocation for secondary user （SU） of cognitive radio （CR） system, it is necessary to consider allocation process from the temporal perspective. In this article, a chain store game is modeled to achieve SU＇s equilibrium state. Due to the computational complexity of solving equilibrium states, the authors explore the correlated equilibrium （CE） by importing signal mechanisms based on time and sequence number. Also, correlated equilibrium based game algorithms are presented. Simulations show that these algorithms are superior to other allocation algorithms both in channel utilization and communication time.
基金partially supported by the US NSF under grant CNS-2105004.
文摘The importance of transaction fees in maintaining blockchain security and sustainability has been confirmed by extensive research,although they are not mandatory in most current blockchain systems.To enhance blockchain in the long term,it is crucial to design effective transaction pricing mechanisms.Different from the existing schemes based on auctions with more consideration about the profit of miners,we resort to game theory and propose a correlated equilibrium based transaction pricing mechanism through solving a pricing game among users with transactions,which can achieve both the individual and global optimum.To avoid the computational complexity exponentially increasing with the number of transactions,we further improve the game-theoretic solution with an approximate algorithm,which can derive almost the same results as the original one but costs significantly reduced time.We also propose a truthful assessment model for pricing mechanism to collect the feedback of users regarding the price suggestion.Extensive experimental results demonstrate the effectiveness and efficiency of our proposed mechanism.
