Reinforcement Learning for Spacecraft Docking
Train an AI agent to autonomously dock with a space station
Last reviewed: March 2026Overview
Autonomous spacecraft docking is one of the grand challenges in space engineering. SpaceX's Dragon capsule already does it, and future cislunar and Mars missions will require even more capable autonomous systems operating with significant communication delays.
In this project, you'll build a custom Gymnasium environment that simulates spacecraft proximity operations in 2D (planar orbital mechanics). Then you'll train a reinforcement learning agent using Stable-Baselines3 to learn a docking policy from scratch — controlling thrusters to approach a target, match velocities, and dock within tolerance.
This combines orbital mechanics, control theory, and modern RL — exactly the intersection that NASA and SpaceX are investing in for next-generation autonomous missions.
What You'll Learn
- ✓ Build a custom OpenAI Gymnasium environment with continuous state and action spaces
- ✓ Implement simplified Clohessy-Wiltshire relative motion dynamics for proximity operations
- ✓ Design reward functions that shape safe, fuel-efficient docking behavior
- ✓ Train RL agents (PPO, SAC) using Stable-Baselines3 and tune hyperparameters
- ✓ Evaluate learned policies for safety, fuel efficiency, and robustness
- ✓ Compare RL-based control against classical linear feedback controllers
Step-by-Step Guide
Study the Dynamics
Review the Clohessy-Wiltshire (CW) equations — the linearized relative motion equations for two spacecraft in nearby circular orbits. These give you the state dynamics: relative position (x, y) and velocity (vx, vy) in the LVLH (Local Vertical Local Horizontal) frame.
Implement the CW equations in Python and simulate free-drift trajectories to build intuition. Orbital mechanics creates non-intuitive behavior: thrust "forward" in orbit actually raises altitude.
Build the Gymnasium Environment
Create a custom gymnasium.Env class with:
- State: [x, y, vx, vy, fuel_remaining] — 5D continuous
- Action: [thrust_x, thrust_y] — 2D continuous, bounded by max thrust
- Dynamics: CW equations with discrete time step (1–10 seconds)
- Initial conditions: randomized start position 100–500m from target
- Success: position within 0.5m and velocity within 0.05 m/s of target
Design the Reward Function
The reward function is the most critical design decision. Start with:
- Distance penalty: -k1 × distance_to_target (encourages approach)
- Velocity penalty: -k2 × relative_velocity when close (encourages gentle approach)
- Fuel penalty: -k3 × thrust_magnitude (encourages efficiency)
- Success bonus: +1000 for achieving docking conditions
- Crash penalty: -500 for excessive approach velocity
Tuning these weights is where the real engineering happens. Too much fuel penalty and the agent won't dock. Too little and it wastes propellant.
Train with PPO
Start with PPO (Proximal Policy Optimization) from Stable-Baselines3 — it's robust and works well for continuous control:
from stable_baselines3 import PPO
Train for 1–5 million timesteps. Monitor episode reward, success rate, and fuel usage. Use TensorBoard for visualization. Training should take 30–60 minutes on a modern CPU.
Evaluate and Visualize
Run 1,000 evaluation episodes with the trained agent. Calculate: success rate, mean fuel consumption, mean docking velocity, and mean time to dock. Plot example trajectories showing the approach path.
Create an animation of the docking maneuver — seeing the agent learn to perform a V-bar approach (approach from the velocity direction) is very satisfying.
Compare Against Classical Control
Implement a linear quadratic regulator (LQR) for the same docking problem. The CW equations are linear, so LQR gives the optimal linear feedback solution. Compare the RL agent against LQR in terms of success rate, fuel usage, and robustness to initial conditions.
Where does RL shine? Typically in handling constraints (fuel limits), non-linear scenarios (large relative distances where CW breaks down), and uncertainty (sensor noise, thruster failures).
Add Complexity
Extend the environment with realistic challenges: sensor noise on position/velocity measurements, thruster dead-bands (minimum impulse), communication delays, or keep-out zones (regions the agent must avoid). Retrain and evaluate how the RL agent adapts.
Career Connection
See how this project connects to real aerospace careers.
Space Operations →
Autonomous proximity operations are the future of satellite servicing, debris removal, and space station resupply
Aerospace Engineer →
RL for control is an active research area in GNC (Guidance, Navigation, and Control) departments at every major space company
Drone & UAV Ops →
The same RL techniques apply to autonomous drone landing, formation flying, and GPS-denied navigation
Astronaut →
Understanding autonomous docking systems — their capabilities and limitations — is essential for astronauts who will supervise these systems
Go Further
Extend your RL + space research:
- 3D dynamics — extend to full 3D CW equations with 6-DOF (position + attitude) control
- Multi-agent — train two agents to dock with each other, or one agent servicing multiple targets
- Sim-to-real transfer — add domain randomization to make the policy robust enough for real hardware
- Publish — RL for spacecraft autonomy is actively published at AIAA SciTech, AAS GN&C, and RL conferences