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Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control

Jacopo Panerati, Hehui Zheng, SiQi Zhou, James Xu, Amanda Prorok, Angela P. Schoellig

arXiv:2103.02142v3cs.ROcs.LG

TL;DR

Robotic simulation and RL benchmarking need tools that combine practical realism with portability, parallelizability, and flexible learning interfaces. The paper introduces an open-source Bullet-based Gym-like environment for multiple quadcopters and demonstrates control, single-agent RL, and multi-agent RL workflows. The environment supports realistic collisions and aerodynamic effects alongside vision-based and multi-agent interfaces, with parallel execution reaching 80× data generation relative to elapsed time.

  • Problem

    Existing simulators can lack portability and parallelizability, while many RL environments trade realism for throughput, limiting support for practical and fairly comparable robotic control research.

  • Method

    The paper develops an open-source, Bullet-based Gym-style environment supporting multi-agent and vision-based quadcopter learning, PID control, and model-based or model-free RL workflows.

  • Results

    The environment supports trajectory tracking, downwash-aware multi-robot flight, and single- and multi-agent stabilization examples; parallel execution generated 80× the data of elapsed time.

  • Takeaways & Limitations

    gym-pybullet-drones provides a shared platform for developing control and reinforcement-learning approaches on multi-quadcopter tasks with realistic physics and flexible interfaces.

Abstract

from arXiv · show

Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications. Reinforcement learning environments -- simple simulations coupled with a problem specification in the form of a reward function -- are also important to standardize the development (and benchmarking) of learning algorithms. Yet, full-scale simulators typically lack portability and parallelizability. Vice versa, many reinforcement learning environments trade-off realism for high sample throughputs in toy-like problems. While public data sets have greatly benefited deep learning and computer vision, we still lack the software tools to simultaneously develop -- and fairly compare -- control theory and reinforcement learning approaches. In this paper, we propose an open-source OpenAI Gym-like environment for multiple quadcopters based on the Bullet physics engine. Its multi-agent and vision based reinforcement learning interfaces, as well as the support of realistic collisions and aerodynamic effects, make it, to the best of our knowledge, a first of its kind. We demonstrate its use through several examples, either for control (trajectory tracking with PID control, multi-robot flight with downwash, etc.) or reinforcement learning (single and multi-agent stabilization tasks), hoping to inspire future research that combines control theory and machine learning.

I. INTRODUCTION

The paper identifies a gap between realistic robotic simulators and high-throughput RL environments, then proposes a compact Gym-style quadcopter environment addressing portability, flexibility, and realism.

  • Research gap: Existing robotic simulators can hinder parallel machine-learning workflows because of limited portability and platform-specific or proprietary dependencies.ROS-, Gazebo-, and Webots-based solutions may be difficult to use in remote, highly parallel computing environments.
  • Research gap: Many RL environments favor high sample throughput over realism, which may limit their representativeness for practical robotic scenarios.The paper frames this as a concern for developing and benchmarking robotics algorithms.
  • Proposed environment: gym-pybullet-drones is a compact, open-source Gym-style environment for defining multi-agent and vision-based learning tasks with one or more nanoquadcopters.It supports both model-free and model-based RL workflows for end-to-end quadcopter control.
  • Proposed environment: The environment supports realistic collisions, aerodynamic effects, and extensible dynamics through Bullet Physics.These features are presented as a core realism advantage of the proposed simulator.
  • Proposed environment: Its interfaces support vision-based RL and multi-agent RL simultaneously, while multiple environments can run in GUI or headless modes with or without a GPU.The software also includes pre-implemented PID control and workflows for Stable Baselines3 and RLlib.

B. Quadcopter Simulators

Existing quadcopter simulators provide varied realism, rendering, and vehicle support, but the paper distinguishes gym-pybullet-drones through integrated Gym, vision-based, and multi-agent capabilities.

  • Existing simulators: RotorS provides multiple AscTec multirotor models and simulated sensors but lacks ready-to-use RL interfaces and may be unsuitable for parallel or vision-based learning.CrazyS extends RotorS for the Crazyflie 2.x but retains these RL-related limitations.
  • Existing simulators: AirSim supports multiple vehicles and photorealistic rendering, but its computational requirements, simplified collisions, and lack of a native Gym interface limit its fit for control learning.
  • Existing simulators: Flightmare combines photorealistic rendering, fast parallel dynamics, Gym’s API, and a single-agent RL workflow, but lacks vision-based and multi-agent Gym interfaces.
  • Positioning: gym-pybullet-drones is presented as a general-purpose multi-agent Gym environment for quadcopters, with the Crazyflie 2.x as its default model.The paper compares its features with CrazyS, AirSim, and Flightmare.

A. Gym Environment Classes

The environment uses OpenAI Gym conventions with PyBullet to simulate quadcopter forces, torques, kinematics, collisions, and aerodynamic effects. It also offers an explicit Python dynamics update for comparison, debugging, and ad hoc modeling.

  • Gym environments receive actions, update internal state, and return observations and rewards within a feedback-loop formulation.
  • PyBullet models the forces and torques acting on each quadcopter and updates the kinematics of all vehicles.
  • The default Crazyflie 2.x model uses URDF files to specify physical properties, inertial parameters, collision geometry, and × or + configurations.
  • Motor forces and yaw torque are proportional to squared motor speeds, while PyBullet supports finer physics stepping than the Gym control frequency.
  • An explicit Python dynamics update computes global-frame linear acceleration and local-frame turn-rate changes independently of Bullet.
  • The environment models drag, ground effect, and other aerodynamic effects separately so they can be used jointly with the basic dynamics.

3) Aerodynamic Effects:

The simulator includes aerodynamic models for drag, ground effect, and downwash. These effects are parameterized using vehicle motion, motor behavior, geometry, distance, and experimentally identified constants, with validation against a real flight experiment.

  • Drag acts opposite the direction of motion and depends on quadcopter velocity, rotor angular velocities, and experimentally derived coefficient matrix kD.
  • Ground effect increases thrust at very low altitude through motor-specific contributions depending on propeller radius, motor speeds, altitudes, and constant kG.
  • Downwash reduces the lift of a lower quadcopter when vehicles cross paths at different altitudes.The modeled contribution depends on relative distances δx, δy, and δz and experimentally identified constants kD1, kD2, and kD3.
  • A flight simulation using the aerodynamic model is compared with data from a real-world flight experiment.

D. Observation Spaces

Observation spaces provide multi-drone kinematic states, neighborhood information, and optional first-person visual inputs. The environment supports configurable action abstractions alongside these observations.

  • Each environment step returns observations containing positions, quaternions, roll, pitch, yaw, linear and angular velocities, and four motor speeds for every drone.
  • A neighbor-aware observation adds each drone’s Boolean adjacency-matrix row for vehicles within radius R.
  • Visual observations can include each drone’s perspective RGB, depth, and segmentation frames.The RGB view has shape 64×48×4, while depth and segmentation views have shape 64×48.
  • The environment offers multiple action implementations because robotic applications require different abstraction levels.
  • The default action space supplies four motor speeds in RPMs for each drone.

2) Desired Velocity Input:

Drones can receive desired velocity vectors instead of direct motor commands. A PID controller translates these higher-level inputs into PWMs and motor speeds, while the modular design supports further customization for reinforcement learning.

  • Desired velocity actions contain a unit-direction vector [vx, vy, vz] and a desired velocity magnitude vM for each drone.
  • The desired velocity input is converted into PWMs and motor speeds by PID position and attitude-control subroutines.
  • The environment’s modular structure is intended to facilitate customizing observation and action spaces for additional RL and MARL applications.
  • Reward functions are task-dependent, while optional done and info returns can implement additional safety requirements.
  • A complete single-agent reinforcement-learning workflow is provided through Stable Baselines3, supporting MLP and CNN policies for kinematic or vision observations.

2) Stable Baselines3 Workflow:

The environment combines Gym’s readable Python interface with Bullet’s performant backend to support efficient, parallel simulation and reproducible control and RL workflows.

  • Stable Baselines3 Workflow: RLlib provides a distributed multi-agent training workflow, including a 2-agent centralized-critic example and replayable best policies.The workflow uses Ray-based distributed applications and supports popular RL and MARL algorithms.
  • Stable Baselines3 Workflow: The environment includes a minimalist ROS2 wrapper that publishes observations and receives actions through separate topics while continuously stepping simulations.
  • Stable Baselines3 Workflow: Gym’s Python API and Bullet’s C++ backend balance readability and portability with computational performance.
  • Stable Baselines3 Workflow: Closed-loop PID control, rather than a stripped-down physics benchmark, was used to generate the computational-performance data.The benchmark removed GUI, debugging, obstacles, and front-end reporting between physics steps.
  • Stable Baselines3 Workflow: Over 15× wall-clock speed-up was achieved for one drone at 240Hz, while parallel execution generated 80× the data of elapsed time.Visual-observation throughput was approximately 750kB/s with TinyRenderer and 10× faster with OpenGL3 acceleration.
  • Stable Baselines3 Workflow: The paper demonstrates joint use of the environment with control approaches and reinforcement-learning algorithms through six examples.The source code is available online and includes installation instructions.

A. Control

The control examples command multiple quadcopters through motor-speed or desired-velocity inputs, demonstrating external and embedded PID control across trajectories and velocity changes.

  • A. Control: The first examples command multiple quadcopters using motor speeds or desired velocity inputs and demonstrate modeled ground-effect and downwash behaviors.
  • A. Control: Four Crazyflies use external PID control, receiving kinematics observations and returning commanded motor speeds.
  • A. Control: A 12-second flight plots positions, velocities, attitude, angular velocity, and motor speeds for all vehicles tracking a circular trajectory.
  • A. Control: Desired velocity inputs are targeted by PID controllers embedded within the Gym environment.
  • A. Control: The embedded controllers produce linear-velocity responses to step-wise changes in velocity inputs.

3) Ground Effect:

The aerodynamic examples examine ground effect and downwash in quadcopter flight, including take-off behavior and simulation agreement with real-world data.

  • 3) Ground Effect: Ground effect produces a small but noticeable take-off overshoot and a larger maximum vertical velocity during the first half-second of simulation.
  • 4) Downwash: The downwash example compares two Crazyflies moving along sinusoidal trajectories at different altitudes under the downwash model.
  • 4) Downwash: Simulation results are compared with experimental data used to identify the downwash-model parameters.
  • 4) Downwash: The simulation closely matches real-world x and z positions.
  • 4) Downwash: The simplified single-contribution downwash model does not fully capture effects on the lower drone’s pitch and resulting x velocity.

B. Reinforcement Learning

The reinforcement-learning examples train single- and multi-agent policies for altitude reaching and hovering using normalized observations and RPM actions, comparing standard Stable Baselines3 algorithms.

  • B. Reinforcement Learning: Single- and multi-agent tasks learn policies to reach a target altitude and hover using normalized kinematics observations and normalized one-dimensional RPM actions.
  • B. Reinforcement Learning: The single-agent task rewards the negation of squared Euclidean distance from a predetermined altitude set point.
  • B. Reinforcement Learning: PPO, A2C, and SAC use Stable Baselines3 defaults with untuned hyperparameters and multilayer perceptron policies.The MLP has four hidden layers with 512, 512, 256, and 128 units using ReLU activations.
  • B. Reinforcement Learning: SAC performs best, while all three algorithms succeed with substantially different learning curves.The comparison deliberately omits parameter tuning to avoid cherry-picking.
  • B. Reinforcement Learning: The multi-agent task trains a leader as in the single-agent task while rewarding a follower for tracking the leader’s altitude.

2) Multi-agent Leader-follower:

The leader-follower example uses RLlib with a centralized critic and separate action models to train stable multi-agent policies. The resulting policies show follower oscillations and erratic commanded motor speeds, while the simulator remains extensible for future multi-agent capabilities.

  • Multi-agent Leader-follower: RLlib implements the workflow with a central critic receiving 25 inputs and two action models receiving 12 inputs each.All models use two hidden layers of 256 units with tanh activations.
  • Multi-agent Leader-follower: Stable training produces successfully trained leader-follower policies, with the follower reflecting the leader’s minor oscillations.The leader’s oscillations are described as minor and are reflected by the follower.
  • Multi-agent Leader-follower: The trained policies command erratic motor RPMs despite successful stabilization.The reported irregularity concerns the RPM commands issued by the policies.
  • Future extensions: The simulator is structured for extensions including heterogeneous teams, richer downwash, symbolic dynamics, and additional MARL frameworks.Proposed enhancements include importing multiple URDF files, more detailed aerodynamic models, CasADi-based dynamics, and PyMARL workflows.
  • Contribution: The environment combines modular Bullet physics, vision-based observations, and a multi-agent reinforcement learning interface for aerial robotics research.The paper positions these features as a bridge between reinforcement learning and control research.
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