Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Reinforcement learning (RL) is essential for the computation of game equilibria and the estimation of payoffs under incomplete information. Hover, it has been a challenge to apply RL-based algorithms in the energy trading game among smart microgrids where no information concerning the distribution of payoffs is  a priori  available and the strategy chosen by each microgrid is private to opponents, even trading partners.  proposes a new energy trading framework based on the repeated game that enables each microgrid to individually and randomly choose a strategy with probability to trade the energy in an independent market so as to maximize his/her average revenue. By establishing the relationship beten the average utility maximization and the best strategy, two learning-automaton-based algorithms are developed for seeking the Nash equilibria to accommodate the variety of situations. The novelty of the proposed algorithms is related to the incorporation of a normalization procedure into the classical linear reward–inaction scheme to provide a possibility to operate any bounded utility of a stochastic character. Finally, a numerical example is given to demonstrate the effectiveness of the algorithms.
['Huiwei Wang', 'Tingwen Huang', 'Xiaofeng Liao', 'Haitham Abu-Rub', 'Guo Chen']
I am writing a research paper on a idea Reinforcement Learning in Energy Trading Game Among Smart Microgrids
[{"from": "human", "value": "I am writing a research paper on a idea Reinforcement Learning in Energy Trading Game Among Smart Microgrids"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Reinforcement learning (RL) is essential for the computation of game equilibria and the estimation of payoffs under incomplete information. Hover, it has been a challenge to apply RL-based algorithms in the energy trading game among smart microgrids where no information concerning the distribution of payoffs is  a priori  available and the strategy chosen by each microgrid is private to opponents, even trading partners.  proposes a new energy trading framework based on the repeated game that enables each microgrid to individually and randomly choose a strategy with probability to trade the energy in an independent market so as to maximize his/her average revenue. By establishing the relationship beten the average utility maximization and the best strategy, two learning-automaton-based algorithms are developed for seeking the Nash equilibria to accommodate the variety of situations. The novelty of the proposed algorithms is related to the incorporation of a normalization procedure into the classical linear reward\u2013inaction scheme to provide a possibility to operate any bounded utility of a stochastic character. Finally, a numerical example is given to demonstrate the effectiveness of the algorithms."}]