Details

Autor(en) / Beteiligte

• Kallstrom, Johan
• Heintz, Fredrik

Titel

Agent Coordination in Air Combat Simulation using Multi-Agent Deep Reinforcement Learning

Ist Teil von

• 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020, p.2157-2164

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Simulation-based training has the potential to significantly improve training value in the air combat domain. However, synthetic opponents must be controlled by high-quality behavior models, in order to exhibit human-like behavior. Building such models by hand is recognized as a very challenging task. In this work, we study how multi-agent deep reinforcement learning can be used to construct behavior models for synthetic pilots in air combat simulation. We empirically evaluate a number of approaches in two air combat scenarios, and demonstrate that curriculum learning is a promising approach for handling the high-dimensional state space of the air combat domain, and that multi-objective learning can produce synthetic agents with diverse characteristics, which can stimulate human pilots in training.