This repository showcases the use of the pyflyt module's rocket landing environment and various reinforcement learning (RL) algorithms to solve it. The TQC (Truncated Quantile Critics) algorithm from Stable Baselines has shown the best performance.

• Introduction
• Installation
• Usage
• Simulation & Inference with Trained Model
• Screenshots
• Video Demonstration
• Training Metrics
• References

This project demonstrates the application of RL algorithms in the rocket landing environment provided by pyflyt. The environment is a challenging testbed for algorithms to learn and perfect the landing of a rocket.

• Python 3.8+
• Poetry

1. Clone the repository

   git clone https://github.com/artemi8/RL-Rocket-Landing.git
   cd RL-Rocket-Landing

2. Install Poetry

   Follow the instructions from the official Poetry documentation.

3. Install dependencies

   poetry install

To use the environment and run your experiments, follow the steps below.

To test the best performing TQC model, follow the instructions provided in the Jupyter notebook located at sample_runs/rocket_landing_experiments-testing.ipynb.

1. Launch Jupyter Notebook

   poetry shell

   jupyter notebook

2. Open and run the notebook

   Navigate to sample_runs/rocket_landing_experiments-testing.ipynb in the Jupyter interface and follow the instructions to run the inference with the trained TQC model.

Watch the rocket landing demonstration:

Go to training metrics.

• PyFlyt Documentation
• Stable Baselines3 Documentation
• Poetry Documentation