With the development of unmanned aerial vehicle (UAV) technology, UAV swarm confrontation has attracted many researchers’ attention. However, the situation faced by the UAV swarm has substantial uncertainty and dynamic variability. The state space and action space increase exponentially with the number of UAVs, so that autonomous decision-making becomes a difficult problem in the confrontation environment. In this paper, a multiagent reinforcement learning method with macro action and human expertise is proposed for autonomous decision-making of UAVs. In the proposed approach, UAV swarm is modeled as a large multiagent system (MAS) with an individual UAV as an agent, and the sequential decision-making problem in swarm confrontation is modeled as a Markov decision process. Agents in the proposed method are trained based on the macro actions, where sparse and delayed rewards, large state space, and action space are effectively overcome. The key to the success of this method is the generation of the macro actions that allow the high-level policy to find a near-optimal solution. In this paper, we further leverage human expertise to design a set of good macro actions. Extensive empirical experiments in our constructed swarm confrontation environment show that our method performs better than the other algorithms.

Nowadays, unmanned aerial vehicles (UAVs) pose a significant challenge to air defense systems. Unmanned combat aerial vehicles (UCAVs) have been proven to be an effective method to counter the threat of UAVs in application. Therefore, maneuver decision-making has become the crucial technology to achieve autonomous air combat for UCAVs. In order to solve the problem of maneuver decision-making, an autonomous model of UCAVs based on the deep reinforcement learning method was proposed in this paper. Firstly, the six-degree-of-freedom (DoF) dynamic model was built in three-dimensional space, and the continuous actions of tangential overload, normal overload, and roll angle were selected as the maneuver inputs. Secondly, to improve the convergence speed for the deep reinforcement learning method, the idea of “scenario-transfer training” was introduced into the twin delayed deep deterministic (TD3) policy gradient algorithm, the results showing that the improved algorithm could cut off about 60% of the training time. Thirdly, for the “nose-to-nose turns”, which is one of the classical maneuvers for experienced pilots, the optimal maneuver generated by the proposed method was analyzed. The results showed that the maneuver strategy obtained by the proposed method was highly consistent with that made by experienced fighter pilots. This is also the first time in a public article that compared the maneuver decisions made by the deep reinforcement learning method with experienced fighter pilots. This research can provide some meaningful references to generate autonomous decision-making strategies for UCAVs.