Matlab DDPG Quadcopter Helix Training

I’ve put together a straightforward quadcopter model in MATLAB & Simulink, but I now need to teach it to follow a smooth helix path using Deep Deterministic Policy Gradient (DDPG). what’s missing is the reinforcement-learning brain and a convincing proof that the aircraft can reproduce the commanded spiral in simulation.

Here’s what I’m looking for you to accomplish:

• Build or adapt a DDPG agent in MATLAB’s Reinforcement Learning Toolbox (or compatible custom code) that takes my existing state-space signals and outputs the four motor thrust commands.
• Train the agent until the quadcopter reliably tracks a parameterised helix trajectory in Simulink. Convergence speed is less important than final accuracy and stability.
• Supply the full training script, tuned hyper-parameters, and the trained network weights so I can reproduce the results.
• Demonstrate success with logged plots or a short Simulink animation showing position error trending to zero over several helix revolutions.

Acceptance criteria
1. Use a single DDPG agent to control end-to-end level ( States to Motor Speed or Ft+Tau xyz)
2. Position error over one helix revolution ≤ 0.01 m in your final run.
3. All .m and .slx files are documented with inline comments.
4. Hyperparameters Tuning with Reasonable Comment.
5. Visual Simulation of Drone tracking helix Trajectory.

If you’ve previously tuned DDPG or similar (TD3/SAC) on aerial vehicles, let me know—quick validation matters more to me than starting from scratch.