Hierarchical reinforcement learning guidance with threat avoidance

The guidance strategy is an extremely critical factor in determining the striking effect of the missile operation. A novel guidance law is presented by exploiting the deep reinforcement learning (DRL) with the hierarchical deep deterministic policy gradient (DDPG) algorithm. The reward functions are constructed to minimize the line-of-sight (LOS) angle rate and avoid the threat caused by the opposed obstacles. To attenuate the chattering of the acceleration, a hierarchical reinforcement learning structure and an improved reward function with action penalty are put forward. The simulation results validate that the missile under the proposed method can hit the target successfully and keep away from the threatened areas effectively.