Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning

Competitive tasks have long served as benchmarks for progress in artificial intelligence. Landmark results have been achieved in domains such as Go [1], poker [2], and real-time strategy games [3], where agents learn to plan, adapt, and compete under structured rules. As research moves from virtual environments to the physical world, robot sports-structured, rule-based competitions involving physical agents-have emerged as a promising frontier for embodied intelligence. Examples include robot soccer [4, 5], table tennis [6, 7], and multi-drone pursuit-evasion [8], which combine high-level strategy with low-level motion control in physically grounded settings. In this paper, we tackle a new embodied competitive task proposed by the V olleyBots testbed [9]: 3v3 multi-drone volleyball. This task exemplifies the structure of a robot sport-well-defined objectives, explicit rules, and head-to-head competition-while presenting a set of unique and underex-plored challenges. Each team must coordinate three quadrotors to rally a ball over a net, switching roles dynamically between offense and defense in a turn-based fashion. The environment is highly dynamic and demands precise timing, agile 3D maneuvering, and strategic team-level behavior. The turn-based nature of ball exchange introduces long-horizon temporal dependencies; the multi-agent setting requires tightly coupled tactics; and the underactuated dynamics of quadrotors call for fine-grained, reactive motor skills.