Search

The authors consider the problem of multi-player evolutionary game with two strategies and an arbitrary number of players. A randomised probabilistic access model is considered where the users randomly select the strategies. The strategies are a selection between two radio access technologies to receive service from. For a fair resource allocation between a number of users, the payoff function of each player is considered to be inversely proportional to the number of players choosing a strategy. It is valid for the radio access technology selection problem where resources are divided fairly between the users. The necessary and sufficient conditions for the existence and uniqueness of a mixed Nash equilibrium point of the game is provided. In addition, it is proved that the mixed Nash equilibrium point of the game is globally asymptotically stable and also evolutionarily stable strategy. [ABSTRACT FROM AUTHOR]

The authors consider the problem of multi‐player evolutionary game with two strategies and an arbitrary number of players. A randomised probabilistic access model is considered where the users randomly select the strategies. The strategies are a selection between two radio access technologies to receive service from. For a fair resource allocation between a number of users, the payoff function of each player is considered to be inversely proportional to the number of players choosing a strategy. It is valid for the radio access technology selection problem where resources are divided fairly between the users. The necessary and sufficient conditions for the existence and uniqueness of a mixed Nash equilibrium point of the game is provided. In addition, it is proved that the mixed Nash equilibrium point of the game is globally asymptotically stable and also evolutionarily stable strategy. [ABSTRACT FROM AUTHOR]

This study addresses a robust counterpart of the deterministic mean field control in a multi‐agent system. A decentralised mean field algorithm is proposed to solve a min–max control problem for a large population of heterogeneous agents. In the proposed leader following scheme, the leader tracks a reference signal which is unknown to the followers and each follower tracks a convex combination of the population state average and the leader's state. The leader plays a robust min–max game against disturbance and the followers play a mean field εN ‐Nash game against each other and at the same time, each follower plays a robust min–max game against the disturbance. For all the players, finite horizon quadratic cost is considered. In the proposed decentralised algorithm, followers do not need the knowledge about each of leader's and other followers' states and they only use an estimate of the population state average. In this way, propose a policy iteration method which guarantees the convergence to the saddle point mean field εN ‐Nash solution. The proposed method is applied to a large population of agents and compared with centralised algorithm to demonstrate the results. [ABSTRACT FROM AUTHOR]