Train a Drone to Hover with PyTorch RL
Implement PPO from scratch and watch a quadrotor learn to balance itself.
Last reviewed: March 2026Overview
Most reinforcement learning practitioners use Stable-Baselines3 or RLlib without understanding what is inside the black box. Implementing PPO from scratch is one of the highest-leverage educational exercises in the field: it forces you to understand every component — the policy gradient theorem, the value function critic, Generalised Advantage Estimation, the KL-penalised clipped surrogate objective, and entropy regularisation. Once you have built it yourself, you can read RL papers fluently, debug training failures systematically, and modify algorithms for new problem structures.
Quadrotor hovering is the ideal testbed: the dynamics are nonlinear but well-understood, the task is clear (minimise position and velocity error at a target point), and a lightweight analytical quadrotor model can be implemented in pure NumPy/PyTorch without a heavy simulator dependency. You will implement a 12-state quadrotor model (position, velocity, Euler angles, angular rates) with a motor speed action space, wrap it in a Gymnasium interface, and train your from-scratch PPO implementation to stabilise the quadrotor at a target hover point in under 2 million steps.
Aerospace applications of RL-based flight control are expanding rapidly — from autonomous air taxi attitude control to spacecraft docking — and the ability to understand, implement, and modify these algorithms at the source code level is a significant differentiator for candidates applying to autonomy engineering roles at aerospace companies and research institutions.
What You'll Learn
- ✓ Implement the PPO algorithm from scratch in PyTorch including actor-critic networks, GAE, and the clipped surrogate loss
- ✓ Build a custom quadrotor physics environment in NumPy wrapped as a Gymnasium interface
- ✓ Diagnose RL training instability using entropy, value loss, and policy gradient norm monitoring
- ✓ Extend hover training to waypoint tracking with curriculum learning across increasing target distances
- ✓ Compare your from-scratch PPO implementation against Stable-Baselines3 PPO on the same environment
Step-by-Step Guide
Implement the quadrotor physics model
Implement a discrete-time 12-state quadrotor model in NumPy: state vector [x, y, z, vx, vy, vz, φ, θ, ψ, p, q, r] and action vector [Ω₁², Ω₂², Ω₃², Ω₄²] (motor RPM squared). Implement the translational dynamics (thrust + gravity), rotational dynamics (torques from differential motor speeds), and Euler angle integration using a fixed 0.01 s timestep. Test by simulating a constant-thrust hover trim condition and verifying the drone maintains constant altitude.
Wrap the model as a Gymnasium environment
Subclass gymnasium.Env with the quadrotor model. Define the observation space (all 12 states plus position error to the target) and action space (4 motor commands, normalised to [−1, 1] around hover trim). Implement a reward function: large negative penalty on crash (altitude < 0 or angles > 60°), a Gaussian reward on position error, and a small penalty on angular rate magnitude. Add a 500-step episode time limit.
Implement actor-critic networks and GAE
Define two PyTorch MLPs: a policy network (actor) outputting a Gaussian distribution mean and log-std for each action dimension, and a value network (critic) outputting a scalar state value. Implement Generalised Advantage Estimation (GAE, λ=0.95, γ=0.99) over collected rollout buffers. Verify GAE numerically by checking the advantage at the last timestep equals the TD error.
Implement the PPO update step
Implement the PPO clipped surrogate loss: compute the probability ratio r(θ) = π_θ(a|s) / π_θ_old(a|s), compute the unclipped and clipped objectives, take the minimum, and add the value function MSE loss and entropy bonus. Run multiple mini-batch gradient update epochs (K=10) per rollout batch, using gradient norm clipping (max norm 0.5). Log each loss component, the mean entropy, and the KL divergence from the old policy per update.
Train to hover and extend to waypoint tracking
Train for 1 million steps on the fixed-target hover task. Plot the episode reward and episode length learning curves. Once the policy successfully hovers (mean reward above a target threshold), implement a curriculum: randomly place the waypoint target within a 0.5 m sphere, then 1 m, then 2 m as training progresses. Train for another 1 million steps and evaluate mean distance-to-target at convergence.
Compare against Stable-Baselines3 and document
Train Stable-Baselines3 PPO on the same environment with matched hyperparameters. Plot the two learning curves on the same axes. Report final mean reward, sample efficiency (steps to reach a threshold reward), and wall-clock training time. Write a code walkthrough document explaining each component of your PPO implementation with references to the original PPO paper equations, then submit both the code and document as your project deliverable.
Career Connection
See how this project connects to real aerospace careers.
Aerospace Engineer →
Autonomy engineers developing RL-based flight controllers need to understand the algorithm internals to debug and certify them — this project builds that foundation.
Drone & UAV Ops →
UAS operators at advanced organisations are expected to understand how onboard autonomy systems learn and what their failure modes are — from-scratch RL knowledge enables this.
Space Operations →
RL is being applied to spacecraft attitude control and proximity operations; the hover controller here is a direct analogue of spacecraft attitude stabilisation.
Astronaut →
Astronauts participating in autonomous rendezvous and docking missions benefit from understanding the RL systems that assist or replace manual piloting.
Go Further
- Implement Soft Actor-Critic (SAC) from scratch as a comparison: SAC's maximum entropy objective often converges faster on continuous control tasks.
- Add observation noise (IMU noise model) and train a recurrent policy (LSTM actor) that infers the true state from noisy measurements.
- Implement domain randomisation: vary motor constants, mass, and inertia randomly each episode and measure the robustness improvement when deploying the policy on a fixed nominal model.
- Export the trained policy to TensorFlow Lite and benchmark inference latency on an embedded processor to assess real-time feasibility.