Optimality in Nash Equilibrium for the Multilevel Environment

Authors- Research Scholar Rakesh Kumar, Associate Professor Dr Krishnandan Prasad

Abstract-We have work done provides game theory applications for two equivalent symmetric matrices and skew symmetric matrices. It may be impossible for agents with non-conflicting interests to learn a portfolio coordinating strategy when there are several perishable equilibria. If the agents get loud payoffs on their own and are unaware of the game, the issue is made worse. Therefore, recognizing the game and learning to play are the two interrelated challenges that multiagent reinforcement learning addresses. We introduce optimum adaptive learning, the first convergent method, in this study to the optimal Nash equilibrium. It is simple to set the algorithm’s parameters to reach convergence, as demonstrated by the convergence proof we present. Our research introduces optimum adaptive learning, which is the initial method that reaches the ideal equilibrium of Nash. We present a proof of convergence and demonstrate how simple it is to adjust the algorithm’s parameters in order to reach convergence.

DOI: /10.61463/ijset.vol.12.issue4.237