Solution for Pursuit-Evasion Game of Agents by Adaptive Dynamic Programming

The paper studies a novel method for real-time solutions of the two-player pursuit-evasion game. The min-max principle is adopted to confirm the Nash equilibrium of the game. As agents in the game can form an Internet of Things (IoT) system, the real-time control law of each agent is obtained by taking a linear-quadratic cost function in adaptive dynamic programming. By introducing the Lyapunov function, we consider the scenario when capture occurs. Since most actual systems are continuous, the policy iteration algorithm is used to make the real-time policy converge to the analytical solution of the Nash equilibrium. Furthermore, we employ the value function approximation method to calculate the neural network parameters without directly solving the Hamilton-Jacobi-Isaacs equation. Simulation results depict the method's feasibility in different scenarios of the pursuit-evasion game.