Distributed Optimal Pursuit-Evasion Strategy of Multiple-Pursuer Single-Evader Game via Reinforcement Learning

In this paper, we investigate pursuit-evasion game problem for multiple-pursuer single-evader using reinforcement learning. A performance function including the states and limited inputs of all players is developed to evaluate the whole system's cost. Then we construct the Hamiltonian equation and design the optimal control polices for the pursuers and the evaders using the Bellman optimization principle. And we present a policy iteration (PI) algorithm to learn the value function and control policies of all players. In order to implement PI algorithm, we construct a neural network to approximate the optimal value function and optimal control strategy. Finally, a simulation example is provided to demonstrate the effectiveness of the algorithm.