We present MuJoCo-Drones-Gym, an open-source Gymnasium-compatible multi-drone environment built on top of the MuJoCo physics engine. Multi-drone environment for RL with MuJoCo, with GPU vectorization, wind models, domain randomization, and curriculum learning.</p>\n","updatedAt":"2026-06-12T03:06:54.695Z","author":{"_id":"68e5138abdb80c260c5cb17f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/68e5138abdb80c260c5cb17f/DF-ITQ18HgfVnxU2gUGaC.png","fullname":"Manan Tayal","name":"tayalmanan","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8989036083221436},"editors":["tayalmanan"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/68e5138abdb80c260c5cb17f/DF-ITQ18HgfVnxU2gUGaC.png"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.08039","authors":[{"_id":"6a2b76a84957fcdd3aac069a","user":{"_id":"68e5138abdb80c260c5cb17f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/68e5138abdb80c260c5cb17f/DF-ITQ18HgfVnxU2gUGaC.png","isPro":false,"fullname":"Manan Tayal","user":"tayalmanan","type":"user","name":"tayalmanan"},"name":"Manan Tayal","status":"claimed_verified","statusLastChangedAt":"2026-06-12T06:56:36.309Z","hidden":false}],"publishedAt":"2026-06-06T00:00:00.000Z","submittedOnDailyAt":"2026-06-12T00:00:00.000Z","title":"MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning","submittedOnDailyBy":{"_id":"68e5138abdb80c260c5cb17f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/68e5138abdb80c260c5cb17f/DF-ITQ18HgfVnxU2gUGaC.png","isPro":false,"fullname":"Manan Tayal","user":"tayalmanan","type":"user","name":"tayalmanan"},"summary":"Robotic simulators are a cornerstone of modern research in aerial robotics, serving both as a vehicle for the development of new control algorithms and as the data source for training reinforcement learning (RL) policies. Yet, existing quadcopter learning environments often face a trade-off between physical fidelity, multi-agent support, and the throughput required by modern deep RL pipelines. In this paper, we present MuJoCo-Drones-Gym, an open-source Gymnasium-compatible multi-drone environment built on top of the MuJoCo physics engine. MuJoCo-Drones-Gym supports an arbitrary number of Bitcraze Crazyflie 2.x nano-quadcopters and exposes a modular API for selecting (i)~the physics model (rigid-body MuJoCo, explicit Python dynamics, or any subset of ground effect, blade drag, and inter-drone downwash), (ii)~the action interface (per-motor RPMs, collective normalized thrust, velocity setpoints, or PID waypoint commands), and (iii)~the observation space (kinematic state vectors, RGB / depth / segmentation cameras, or neighbourhood adjacency information). A PettingZoo ParallelEnv wrapper enables drop-in multi-agent reinforcement learning, while a suite of seven task environments, hover, velocity tracking, multi-drone hover, waypoint navigation, formation flight, gate racing, and a generic multi-agent template, demonstrates the breadth of the interface. 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MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning
Abstract
A Gymnasium-compatible multi-drone simulation environment built on MuJoCo physics engine that supports flexible physics models, action interfaces, and observation spaces for reinforcement learning applications.
Robotic simulators are a cornerstone of modern research in aerial robotics, serving both as a vehicle for the development of new control algorithms and as the data source for training reinforcement learning (RL) policies. Yet, existing quadcopter learning environments often face a trade-off between physical fidelity, multi-agent support, and the throughput required by modern deep RL pipelines. In this paper, we present MuJoCo-Drones-Gym, an open-source Gymnasium-compatible multi-drone environment built on top of the MuJoCo physics engine. MuJoCo-Drones-Gym supports an arbitrary number of Bitcraze Crazyflie 2.x nano-quadcopters and exposes a modular API for selecting (i)~the physics model (rigid-body MuJoCo, explicit Python dynamics, or any subset of ground effect, blade drag, and inter-drone downwash), (ii)~the action interface (per-motor RPMs, collective normalized thrust, velocity setpoints, or PID waypoint commands), and (iii)~the observation space (kinematic state vectors, RGB / depth / segmentation cameras, or neighbourhood adjacency information). A PettingZoo ParallelEnv wrapper enables drop-in multi-agent reinforcement learning, while a suite of seven task environments, hover, velocity tracking, multi-drone hover, waypoint navigation, formation flight, gate racing, and a generic multi-agent template, demonstrates the breadth of the interface. We describe the environment design, the underlying physics and quadcopter dynamics, and illustrate its use through control and learning examples that mirror those of the closely related gym-pybullet-drones project, while taking advantage of MuJoCo's improved contact handling, rendering, and parallelizability.
Community
We present MuJoCo-Drones-Gym, an open-source Gymnasium-compatible multi-drone environment built on top of the MuJoCo physics engine. Multi-drone environment for RL with MuJoCo, with GPU vectorization, wind models, domain randomization, and curriculum learning.
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Cite arxiv.org/abs/2606.08039 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.08039 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2606.08039 in a Space README.md to link it from this page.
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