Actor-critic Based Graphical Games for Discrete-time Linear Systems with Input Constraints

In dynamic graphical games, in order to obtain the optimal strategy for each agent, the traditional method is to solve a set of coupled HJB equations. It is very difficult to solve such problems by traditional methods, especially the input of each agent is constrained. Actor-critic is a reinforcement learning method that can solve such problems through online iteration. This paper proposes an online iterative algorithm for solving linear discrete-time systems graphics games with input constraints, and this algorithm without the need for drift dynamics of agents. Each agent needs two neural networks to fit the agent's value function and control strategy, respectively. Finally, a simulation example is given to show the effectiveness of our method.