Hierarchical reinforcement learning guidance with threat avoidance

Abstract

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.

A Hierarchical Reinforcement Learning Framework for UAV Path Planning in Tactical Environments

“…Another recent work that proposes an HRL-based solution to missile guidance problem with threat-region (i.e., circular nofly zones) avoidance is that of Bohao et.al. [46], who use an improved version of hierarchical deep deterministic policy gradient (DDPG) algorithm. From a problem-context point of view their objective has some similarities to ours since it involves navigation to a target (although using UAVs) and threat avoidance.…”