JSBSim Reinforcement Learning: Build an Autopilot
Train an agent to fly a real aircraft model — six degrees of freedom included.
Last reviewed: September 29, 2026Guide and API references reviewed September 29, 2026. This is an implementation project; training results depend on the environment you build.
Overview
Build a custom Gymnasium environment around JSBSim, then train and evaluate an autopilot for altitude and heading control with Stable-Baselines3. You will implement the environment, choose observations and rewards, and compare PPO and SAC policies on held-out scenarios.
Is this a gym-jsbsim tutorial?
The gym-jsbsim project supplies existing JSBSim environments using the older OpenAI Gym interface. This project has you write your own wrapper using Gymnasium's current custom-environment API. Reading gym-jsbsim can help you understand task design, but its older examples are not drop-in Gymnasium code.
What you need
Python, NumPy, basic flight dynamics and familiarity with reinforcement learning. Use JSBSim's official package and documentation and Stable-Baselines3's environment guide. Save the installed versions with your experiment.
What you will produce
A reproducible environment, saved policy checkpoints, learning curves, altitude/heading error plots, and a short report comparing performance and failure cases. Start with the Cessna model and a small smoke test before committing to long training runs.
What You'll Learn
- ✓Implement a custom Gymnasium environment wrapping a high-fidelity physics simulator (JSBSim)
- ✓Design a reward function that captures altitude hold, heading hold, and control smoothness objectives
- ✓Train PPO and SAC agents using Stable-Baselines3 and interpret learning curves and episode statistics
- ✓Evaluate trained agents on unseen initial conditions and compare robustness across algorithms
- ✓Visualise 3D flight trajectories and analyse failure modes where the policy breaks down
Step-by-Step Guide
Install JSBSim and verify the Python bindings
Install the jsbsim Python package and verify it by running the bundled Cessna 172 simulation for 60 seconds and printing altitude and airspeed at each step. Read the JSBSim property tree documentation to understand how to get and set properties (control surface deflections, engine throttle, atmospheric conditions) via the Python API. Select the aircraft model you will use for the project.
Implement the custom Gymnasium environment
Subclass gymnasium.Env and implement __init__, reset, step, and render. In reset, initialise JSBSim with a random starting altitude (2,000–4,000 ft), airspeed (80–120 knots), and heading. Call super().reset(seed=seed) and return (observation, info) from reset. In step, map the RL action to aileron, elevator, rudder and throttle, advance a documented number of simulator steps, and return (observation, reward, terminated, truncated, info). Distinguish task termination from a time-limit truncation. Run stable_baselines3.common.env_checker.check_env(env) and a short random-action rollout before training. Define the observation space as a Box with altitude error, airspeed error, pitch, roll, heading error, and the three angular rates as components.
Design and tune the reward function
Implement a shaped reward: a large negative penalty for exceeding structural limits (bank angle > 60°, airspeed outside the envelope), a Gaussian reward centred on zero altitude error, a Gaussian reward centred on zero heading error, and a small negative penalty proportional to control input magnitude to encourage smooth flight. Test the reward manually by running a deterministic control input sequence and verifying the numerical reward values are in the expected range before starting RL training.
Train PPO and SAC agents
After a short smoke test, use Stable-Baselines3 to train a PPO agent (MlpPolicy, 2×256 hidden layers); 2 million environment steps is a starting experiment budget, not a convergence guarantee. Use Monitor to record episode returns and lengths, and EvalCallback to evaluate periodically (for example, every 50,000 steps). Repeat with SAC (also 2M steps). Use a separate evaluation environment, log seeds and installed versions, and save checkpoints so you can compare intermediate policies. Evaluate a simple baseline controller as well as the learned policies.
Evaluate and compare agents
Load the best checkpoint for each algorithm and run 50 evaluation episodes with randomised initial conditions. Compute mean and standard deviation of cumulative reward, episode survival time (episodes end on out-of-envelope violation), and RMS altitude and heading error over the episode. Plot learning curves side by side and create a summary table comparing PPO and SAC on all metrics.
Visualise trajectories and analyse failure modes
Record state trajectories for 10 representative evaluation episodes per agent. Plot 3D flight paths using Matplotlib and overlay altitude and heading error time series. Identify the initial conditions or wind perturbations (add wind gust noise in step 6 of training and repeat) that cause the policy to fail. Write a failure mode analysis section explaining what state combinations the agent has not generalised to and how the reward function or training distribution could be improved.
Career Connection
See how this project connects to real aerospace careers.
Aerospace Engineer →
Autonomous flight control engineers at UAM companies and defence contractors use RL-in-simulator workflows identical to this project for initial policy development.
Pilot →
Understanding how autopilot and autonomy systems are trained gives pilots insight into the failure modes and limitations of automation they rely on daily.
Drone & UAV Ops →
Fixed-wing UAV autopilot development increasingly uses RL for attitude control and path following; this project provides a directly applicable template.
Flight Dispatcher →
Dispatchers approving autonomous flight operations benefit from understanding how RL autopilots are evaluated and what their performance envelopes look like.
Go Further
- Add turbulence and wind shear to the JSBSim environment and retrain; compare the wind-robust policy against the nominal-only trained policy in windy conditions.
- Implement a curriculum learning schedule that starts with small perturbations and gradually increases initial condition randomisation as the agent improves.
- Extend the task to a full approach-and-landing task: the agent must descend to pattern altitude, align with a runway heading, and achieve a stable ILS-like glidepath.
- Export the trained policy to ONNX and integrate it into a ROS 2 node that commands a simulated aircraft in Gazebo via MAVLink.