utiasDSL/gym-pybullet-drones
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
This project offers a simulated environment for developing and testing control algorithms for single and multi-agent quadcopters. It takes in control commands or reinforcement learning policies and outputs realistic drone behavior, allowing researchers and roboticists to experiment with flight dynamics, formation flying, and autonomous navigation.
1,885 stars. Actively maintained with 2 commits in the last 30 days.
Use this if you are a robotics researcher or control engineer developing and evaluating drone control strategies using simulated environments like PyBullet and Gymnasium.
Not ideal if you need a GPU-accelerated, differentiable JAX-based simulation, symbolic dynamics with constraints, or a production-grade ROS2 deployment with real-world sensors.
Stars
1,885
Forks
524
Language
Python
License
MIT
Category
Last pushed
Feb 15, 2026
Commits (30d)
2
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