An 2D environment for training control policies for a quadrotor drone (technically a planar birotor). Right now, only Soft-Actor-Critic (SAC) is implemented but I may revisit later (there are some failed PID and TD3 attempts :)).

1. Create a virtual environment (I recommend venv).
2. Run .\venv\Scripts\activate to enter the virtual environment
3. Install the dependencies with pip install -r requirements.txt
4. To train a new SAC policy from scratch, edit src/main.py so that eval=False, load=False, render=False. Training rewards will be logged and checkpoints will be saved periodically. Training can be further configured in Agents/sac/run.py.
5. To load a model, place the 3 checkpoints in the Agents/sac/load directory and specify load=True in run.py.
6. I've supplied my best weights (trained on cpu...) in releases.

The drone environment runs on Pygame and can be found in drone/DroneEnv.py. I tried my best to make it Gym compliant, but there are likely things I've missed.

• PID
• LQR
• MPC
• TD3
• Prioritized Replay
• LA3P
• Obstacles and LiDaR simulation for POMDP (using an LSTM based network)
• State estimation
• 3D env rendered in 3JS.

• Inspiration for the environment came from John Buffer's AutoDrone which uses a genetic algorithm to train the policy.
• The SAC implementation was primarily referenced from toshkiwa