Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering

TL;DR

This paper introduces a hierarchical multi-agent reinforcement learning framework for air combat, effectively managing complex decision-making with heterogeneous agents and layered policies.

Contribution

It presents a novel hierarchical structure dividing decision-making into macro and micro levels, with curriculum learning and self-play for training low-level policies.

Findings

Hierarchical framework improves decision accuracy in air combat scenarios.

Curriculum learning enhances training efficiency for low-level policies.

Empirical results validate the effectiveness of the proposed approach.

Abstract

The application of artificial intelligence to simulate air-to-air combat scenarios is attracting increasing attention. To date the high-dimensional state and action spaces, the high complexity of situation information (such as imperfect and filtered information, stochasticity, incomplete knowledge about mission targets) and the nonlinear flight dynamics pose significant challenges for accurate air combat decision-making. These challenges are exacerbated when multiple heterogeneous agents are involved. We propose a hierarchical multi-agent reinforcement learning framework for air-to-air combat with multiple heterogeneous agents. In our framework, the decision-making process is divided into two stages of abstraction, where heterogeneous low-level policies control the action of individual units, and a high-level commander policy issues macro commands given the overall mission targets. Low-level policies are trained for accurate unit combat control. Their training is organized in a learning curriculum with increasingly complex training scenarios and league-based self-play. The commander policy is trained on mission targets given pre-trained low-level policies. The empirical validation advocates the advantages of our design choices.