PyBullet

PyBullet is a Python wrapper based on the Bullet Physics SDK, developed and maintained by Erwin Coumans, and was initially promoted by the Google Brain team for reinforcement learning research.

PyBullet is a Python wrapper for the Bullet physics engine, lightweight and easy to use, commonly used for teaching and rapid prototype development.

In simulation environments like PyBullet, the cost of interaction is low, so the sample-efficiency disadvantage of on-policy methods is weakened and the stability advantage is more prominent.

PyBullet provides lightweight physics simulation, supports rigid body dynamics and collision detection, and has a friendly Python interface.

That an individual developer can achieve a smooth gait and attempt a single-leg stand benefits from the proliferation in recent years of low-cost, high-performance servo motors, inertial sensors, and open-source control libraries (such as ROS 2 and the PyBullet simulation environment).

PyBullet is a lightweight physics simulation engine that supports rigid-body dynamics, joint constraints, and sensor simulation, and is commonly used to validate gait algorithms without bearing the risk of damaging real hardware.

PyBullet is an open-source physics simulation engine capable of simulating rigid body dynamics, collision detection, and sensor feedback

The researcher uses two open-source tools, Stable Baselines3 and PyBullet, for drone reinforcement learning

Developers typically first train models in physics simulation environments (such as Isaac Gym, MuJoCo, or PyBullet).

Mainstream physics engines such as MuJoCo, Isaac Gym, and PyBullet differ considerably in contact modeling, joint friction, and rigid-body dynamics.

Mainstream physics engines include MuJoCo (high-precision contact dynamics), PyBullet (open-source lightweight), Isaac Gym (GPU-parallel acceleration), and Gazebo (ROS ecosystem integration), among others.

gym-pybullet-drones is an open-source simulation environment built specifically for drone reinforcement learning, based on the PyBullet physics engine

PyBullet's physical fidelity is somewhat compromised compared to Gazebo, and its sensor modeling is not sufficiently rich

The same hill-climbing task requires 1 hour and 45 minutes to complete training on PyBullet with a CPU.

For an undergraduate thesis drone MARL project, PyBullet is more suitable because of its gentle learning curve, low hardware requirements (runs smoothly on an RTX 4060), and mature community, enabling the team to enter the algorithm development phase more quickly.

Training with on-policy algorithms such as PPO typically requires 10 million to 50 million steps, and for a scale of 3–5 drones, PyBullet's CPU serial simulation speed is already sufficient.

For undergraduate graduation projects, it is recommended to first complete core algorithm validation with PyBullet, and attempt migration to Isaac Lab for comparative experiments only if time permits.

The Bullet engine used by PyBullet employs a Sequential Impulse solver to handle rigid-body contacts and constraints, advancing the simulation at a fixed timestep (typically 240 Hz, approximately 4.17 ms).

For projects involving 3–5 drones, PyBullet's serial simulation speed on CPU is sufficient to support the training requirements of on-policy algorithms such as PPO, and the acceleration advantage of GPU parallelization is not significant.

gym-pybullet-drones is an open-source drone simulation environment based on the PyBullet physics engine, developed and maintained by a team at the University of Toronto, and is compatible with the OpenAI Gymnasium interface standard.