AI Drone Control with Reinforcement Learning

Classic PID controller is reliable but struggles with sharp maneuvers, strong wind, or landing on moving platforms. We solve these using reinforcement learning (RL): train the drone on millions of flights in the AirSim simulator, then transfer the policy to a real drone. This saves up to 1.5 million RUB on field tests. Here's how it works in practice.

Why RL Over PID?

PID controllers work well under normal conditions, but their parameters are fixed. RL adapts to unexpected situations: motor failure, wind gusts of 10 m/s, or a suddenly shifted target. In tests, our RL policy shows 30% lower tracking error under wind gusts up to 12 m/s compared to PID. For aggressive maneuvers (flip, racing), RL outperforms PID by far—traditional controllers simply cannot perform such moves.

How We Train the Policy in Practice

Simulation Environment

We use AirSim + AirGen for photorealistic scenes. Domain randomization: random wind (0–15 m/s), sensor noise (±2 cm for LiDAR), drone mass variations (1.2–1.8 kg). This is critical for sim-to-real transfer.

Policy Architecture

For hover/navigation—MLP with 256 neurons. For vision-based obstacle avoidance—CNN encoder (depth image) + MLP. Under partial observability (wind is not directly visible), we add an LSTM layer to memorize environment dynamics. A typical policy runs at 50 Hz with p99 latency under 10 ms on Jetson Orin.

Reward Function

Balances goal achievement, collision avoidance, and energy efficiency. Example for navigation:

def compute_reward(self, state, target_pos):
    drone_pos = np.array([...])  # drone position
    dist_to_goal = np.linalg.norm(drone_pos - target_pos)
    reward = -dist_to_goal * 0.1

    if dist_to_goal < 0.5:
        reward += 100.0

    collision = self.client.simGetCollisionInfo()
    if collision.has_collided:
        reward -= 200.0

    velocity = state.kinematics_estimated.linear_velocity
    speed = np.sqrt(velocity.x_val**2 + velocity.y_val**2 + velocity.z_val**2)
    reward -= speed * 0.01  # small speed penalty

    return reward

Sim-to-Real Transfer

Main challenge is the reality gap. We use three methods: system identification (measure real thrust curves and moments of inertia with ±5% accuracy), domain randomization (wide range of physical parameters), and residual policy learning (PID + RL correction). The latter is especially effective—RL fixes PID errors without full replacement, increasing reliability to 95% of scenarios.

What Problems Does RL Solve?

Trajectory tracking with disturbances. Wind gusts up to 12 m/s, sensor noise, motor failures—RL agent adapts where PID loses stability. Aggressive maneuvers: flips, flying at max speed 10 m/s through gates (drone racing). Classical controllers fail under aggressive maneuvers—RL policy learns directly. Landing on a moving platform: ship/car as landing platform with landing accuracy under 10 cm. Relative navigation via AprilTag or ArUco markers.

How We Work on the Project

1. Analysis and specification: Define use cases, boundary conditions (wind, maneuvers, landing accuracy).
2. Simulation design: Configure AirSim/Gazebo for your platform, model sensors and wind.
3. Policy training: PPO or SAC, hyperparameter optimization, thousands of trials. Average training time in simulation—500 episodes (about 2 hours on RTX 4090).
4. Sim-to-real transfer: Calibration, domain randomization, tests on real drone (at least 50 flights).
5. GCS integration: MAVLink, QGroundControl, companion computer communication.
6. Documentation and training: Architecture description, startup guide, monitoring dashboard.
7. 3-month support: Assistance in operation and fine-tuning.

Our Experience and Guarantees

Our team has 5+ years in AI/ML, with projects completed for drone racing and oil rig inspections. We guarantee policy stability under specification conditions. Our engineers are certified in PX4 and ROS2. We'll assess your project and propose the optimal solution—contact us for a consultation.

Estimated Timelines

• Basic navigation (hover + movement): 10–14 weeks.
• Obstacle avoidance + aggressive maneuvers: 20–28 weeks.
• Landing on a moving platform: 16–24 weeks.

The cost is calculated individually after analyzing your scenario and hardware requirements. Get a consultation—write to us, and we will evaluate your project in 1–2 days.

Reinforcement Learning: PPO, SAC, DQN and Industrial Applications

We see projects every day that fail not because of a weak algorithm, but because of incorrect rewards. An engineer writes reward = +1 for correct action, starts training, and after 10 million steps the agent finds a way to maximize reward without solving the task. This is reward hacking — a systemic pain of industrial RL. Our experience shows: proper reward accounts for 70% of success.

Why is RL harder than supervised learning?

In supervised learning, there is a dataset with correct answers. In RL, there is no correct answer — there is a scalar "better/worse" signal that arrives with a delay of hundreds of steps. The agent explores the space and finds a strategy on its own.

Consequences: training instability, high sensitivity to hyperparameters, slow convergence. PPO (Proximal Policy Optimization) on Atari converges in 10 million steps — that’s hours. On robotic tasks with real physics — days or weeks in simulation.

Algorithm selection by task:

How to tune PPO and avoid common problems?

PPO is the workhorse of RL. The main idea: limit policy updates via ratio clipping clip_range=0.2. This provides stability compared to vanilla policy gradient. But without proper tuning, the agent does not converge.

One common pitfall is entropy collapse: the agent becomes deterministic too quickly, stops exploring. Symptom — entropy coefficient drops to zero. Cure — ent_coef=0.01–0.05 and do not lower below 0.001. Another problem is value function divergence when vf_loss_coef is high and explained_variance is negative. We recommend vf_coef=0.5 and gradient clipping max_grad_norm=0.5.

Incorrect n_steps also breaks training. n_steps=2048 is Stable-Baselines3 default. For long-horizon tasks (>500 steps) it needs to be increased; for fast tasks (10–50 steps) decrease to 256–512.

For quick start, use stable-baselines3 + sb3-contrib. For research and custom algorithms — tianshou or CleanRL.

SAC for continuous control

SAC (Soft Actor-Critic) adds entropy maximization to the objective — the agent learns to be both efficient and diverse. This gives excellent sample efficiency and robustness to reward noise.

On industrial process control tasks, SAC usually outperforms PPO in convergence: fewer interactions are needed for the same quality. The key parameter is target_entropy. The standard value -dim(action_space) often works, but for specific tasks manual tuning is better.

How to transfer a trained agent to a real device?

Training RL on a real robot is expensive and dangerous. Standard approach: train in simulation → transfer to real hardware. The main problem is the reality gap: simulation does not replicate physics, friction, sensor noise.

The primary tool is domain randomization. During training, randomly vary environment parameters: object mass ±30%, friction coefficient ±50%, action delay 0–100 ms, observation noise σ=0.01–0.1. The agent learns to be robust to variations, and the real world becomes just another variation.

Comparison of popular simulators:

RLHF: training LLMs from human feedback

RLHF became the standard after InstructGPT. Classic scheme: supervised fine-tuning → reward model → PPO.

Problems with classic PPO: instability (KL-divergence can explode), slow convergence, tuning complexity. Hence popular alternatives:

• DPO — bypasses reward model, learns from preference pairs. Simpler, more stable, but less flexible.
• GRPO — used in DeepSeek-R1, good for reasoning tasks.
• ORPO — combines SFT and alignment into one stage.

The trl library from Hugging Face is the standard. Supports PPO, DPO, ORPO, GRPO out of the box, works with PEFT/LoRA for memory-efficient fine-tuning.

"Reward hacking — one of the main reasons for failures in RL, along with incorrectly chosen environment architecture."

What is included in the work

• Architectural solution and justification of algorithm selection
• Development and documentation of the reward function
• Creating a simulator or configuring an existing one
• Training, hyperparameter sweep (Optuna / Ray Tune)
• Transfer to real hardware or integration into product
• Documentation, access to code and simulators
• Team training and 3-month support after deployment

Work process

1. Task audit — define goals, resources, constraints.
2. Reward engineering — formalize desired behavior, check for reward hacking.
3. Environment and algorithm selection — baseline, first runs.
4. Systematic hyperparameter sweep — use Optuna.
5. Training in simulation with domain randomization.
6. Testing on real equipment (if necessary).
7. Deployment, monitoring, support.

Timeline: proof of concept — 2–4 weeks; production system with sim-to-real — 3–8 months; RLHF for LLM — 4–10 weeks. Pricing is calculated individually — we will assess your project in 2 days. Contact us for a consultation.

Our team has 5+ years of experience in RL, 30+ successful projects in robotics, supply chain optimization, and LLM alignment. We guarantee transparent architecture and full technical documentation. Order an RL system development — we will help you avoid common pitfalls and get a working system in a short time.