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Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning
arXiv – CS AI|Ruize Zhang, Sirui Xiang, Zelai Xu, Feng Gao, Shilong Ji, Wenhao Tang, Wenbo Ding, Chao Yu, Yu Wang||3 views
🤖AI Summary
Researchers developed Hierarchical Co-Self-Play (HCSP), a reinforcement learning framework that enables teams of drones to learn complex 3v3 volleyball through a three-stage training process. The system achieved an 82.9% win rate against baselines and demonstrated emergent team behaviors like role switching and coordinated formations.
Key Takeaways
- →HCSP framework separates high-level strategic decision-making from low-level motion control in multi-drone coordination.
- →The three-stage training pipeline enables both strategy and skills to emerge without expert demonstrations.
- →System achieved 82.9% win rate against non-hierarchical baselines and 71.5% against two-stage variants.
- →Training resulted in emergent team behaviors including role switching and coordinated formations.
- →The research addresses challenges in long-horizon dependencies and tight inter-agent coupling in drone control.
#reinforcement-learning#multi-agent#hierarchical-learning#drone-control#self-play#robotics#coordination#embodied-ai
Read Original →via arXiv – CS AI
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