gym-pybullet-drones
by utiasDSL
PyBullet Gymnasium drone environments for single- and multi-agent RL
Summary
Provides PyBullet + Gymnasium environments for single- and multi-agent quadcopter control. Exposes configurable physics, sensors, and reward setups so you can train and evaluate RL agents in realistic simulated flight scenarios, enabling reproducible experiments via the Planning Pattern. Includes multi-agent scenarios and baselines that make it straightforward to reproduce control experiments and failure cases, leveraging the Event-Driven Agent Pattern for coordination.
Key Benefits
Ideal For
Researchers and engineers who need reproducible drone simulation for training, testing, and evaluating single- or multi-agent control policies, supported by the Agent Protocol for consistent tooling and integration.
Applications
- Validate multi-agent coordination and collision-avoidance strategies in simulation before deployment
- Benchmark and compare reinforcement-learning flight controllers using shared environments
- Reproduce and analyze agent failure modes and robustness under different physics and sensor settings