mlsanityData to 14 Oct 2026

Curriculum-Based Iterative Self-Play for Scalable Multi-Drone Racing

arXiv 2510.22570 ↗First posted 26 Oct 2025cs.RO cs.AI cs.MAcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

The coordination of multiple autonomous agents in high-speed, competitive environments represents a significant engineering challenge. This paper presents CRUISE (Curriculum-Based Iterative Self-Play for Scalable Multi-Drone Racing), a reinforcement learning framework designed to solve this challenge in the demanding domain of multi-drone racing. CRUISE overcomes key scalability limitations by synergistically combining a progressive difficulty curriculum with an efficient self-play mechanism to foster robust competitive behaviors. Validated in high-fidelity simulation with realistic quadrotor dynamics, the resulting policies significantly outperform both a standard reinforcement learning baseline and a state-of-the-art game-theoretic planner. CRUISE achieves nearly double the planner's mean racing speed, maintains high success rates, and demonstrates robust scalability as agent density increases. Ablation studies confirm that the curriculum structure is the critical component for this performance leap. By providing a scalable and effective training methodology, CRUISE advances the development of autonomous systems for dynamic, competitive tasks and serves as a blueprint for future real-world deployment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 24 references that are in the map. First posted on arXiv 26 Oct 2025.

Builds on

11 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Multi-Task Reinforcement Learning for Quadrotors17 Dec

Other references · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  3. 2020Flightmare: A Flexible Quadrotor Simulatorcited by 84
  4. 2019Deep Drone Racing: From Simulation to Reality with Domain Randomizationcited by 76
  5. 2018Deep Drone Racing: Learning Agile Flight in Dynamic Environmentscited by 34
  6. 2019FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulationcited by 22
  7. 2018A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racingcited by 21
  8. 2023Contrastive Initial State Buffer for Reinforcement Learningcited by 8
  9. 2024Multi-Agent Training for Pommerman: Curriculum Learning and Population-based Self-Play Approachcited by 3
  10. 2024SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planningcited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025SPIRAL: Self-Play Incremental Racing Algorithm for Learning in Multi-Drone Competitions
  2. 2025Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning
  3. 2026Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles
  4. 2024Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning
  5. 2025Agile Flight Emerges from Multi-Agent Competitive Racing
  6. 2025VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play
  7. 2026Bridging Performance and Generalization in Reinforcement Learning for Agile Flight
  8. 2026Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning ↗