Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning

TL;DR

This paper introduces a hierarchical multi-agent reinforcement learning framework for simulating and analyzing aerial combat, improving decision-making and training efficiency in complex, multi-agent scenarios.

Contribution

It proposes a novel hierarchical RL approach that separates control and command tasks, enabling scalable training and effective decision-making in simulated air combat.

Findings

Hierarchical structure improves training efficiency.

Framework effectively handles complex flight dynamics.

Empirical results confirm advantages over non-hierarchical methods.

Abstract

This work presents a Hierarchical Multi-Agent Reinforcement Learning framework for analyzing simulated air combat scenarios involving heterogeneous agents. The objective is to identify effective Courses of Action that lead to mission success within preset simulations, thereby enabling the exploration of real-world defense scenarios at low cost and in a safe-to-fail setting. Applying deep Reinforcement Learning in this context poses specific challenges, such as complex flight dynamics, the exponential size of the state and action spaces in multi-agent systems, and the capability to integrate real-time control of individual units with look-ahead planning. To address these challenges, the decision-making process is split into two levels of abstraction: low-level policies control individual units, while a high-level commander policy issues macro commands aligned with the overall mission targets. This hierarchical structure facilitates the training process by exploiting policy symmetries of individual agents and by separating control from command tasks. The low-level policies are trained for individual combat control in a curriculum of increasing complexity. The high-level commander is then trained on mission targets given pre-trained control policies. The empirical validation confirms the advantages of the proposed framework.