The right to claim for damages for infringement is of the character of credit and an object of limitation of action. In case of trademark right infringement, the provision on limitation of action of the General Pr... The right to claim for damages for infringement is of the character of credit and an object of limitation of action. In case of trademark right infringement, the provision on limitation of action of the General Principles of the Civil Law also apply to the trademark proprietor's right to claim for damages for infringement.……展开更多
The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the suffic...The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.展开更多
文摘 The right to claim for damages for infringement is of the character of credit and an object of limitation of action. In case of trademark right infringement, the provision on limitation of action of the General Principles of the Civil Law also apply to the trademark proprietor's right to claim for damages for infringement.……
基金This work was supported by the National Natural Science Foundation of China(No.U1866206).
文摘The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.
