Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering

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.Comment: 22nd International Conference on Machine Learning and Applications (ICMLA 23

Coordinating Team Tactics for Swarm-vs.-Swarm Adversarial Games

While swarms of UAVs have received much attention in the last few years, adversarial swarms (i.e., competitive, swarm-vs.-swarm games) have been less well studied. In this dissertation, I investigate the factors influential in team-vs.-team UAV aerial combat scenarios, elucidating the impacts of force concentration and opponent spread in the engagement space. Specifically, this dissertation makes the following contributions: (1) Tactical Analysis: Identifies conditions under which either explicitly-coordinating tactics or decentralized, greedy tactics are superior in engagements as small as 2-vs.-2 and as large as 10-vs.-10, and examines how these patterns change with the quality of the teams' weapons; (2) Coordinating Tactics: Introduces and demonstrates a deep-reinforcement-learning framework that equips agents to learn to use their own and their teammates' situational context to decide which pre-scripted tactics to employ in what situations, and which teammates, if any, to coordinate with throughout the engagement; the efficacy of agents using the neural network trained within this framework outperform baseline tactics in engagements against teams of agents employing baseline tactics in N-vs.-N engagements for N as small as two and as large as 64; and (3) Bio-Inspired Coordination: Discovers through Monte-Carlo agent-based simulations the importance of prioritizing the team's force concentration against the most threatening opponent agents, but also of preserving some resources by deploying a smaller defense force and defending against lower-penalty threats in addition to high-priority threats to maximize the remaining fuel within the defending team's fuel reservoir.Ph.D