Augmented Auto-Encoder Training Code

Code used to train an augmented auto-encoder (aka denoising auto-encoder with more augmentations) for the DonkeyCar simulator.

Presentation: Learning To Race in Hours

Record data

1. Download and launch the donkey car simulator

2. Install dependencies

   # Install current package
   pip install -e .
   # If not using custom donkey car gym
   pip install git+https://github.com/araffin/gym-donkeycar-1@feat/live-twitch

3. Drive around randomly (make sure to check the script first)

python record_data.py --max-steps 10000 -f logs/dataset-mountain

Train the AutoEncoder

0. [Optional, only a folder with images is required] Split video into a sequence of images

   python -m ae.split_video -i logs/videos/video.mp4 -o logs/dataset/

1. Train the autoencoder (with data-augmentation)

   
   python -m ae.train_ae --n-epochs 100 --batch-size 8 --z-size 32 -f logs/dataset-test/ --verbose 0

You can train on multiple datasets easily:

python -m ae.train_ae --n-epochs 200 --z-size 32 -f logs/dataset-0/ logs/dataset-1/ --batch-size 4

2. Have a coffee while the autoencoder is training ;)

3. [Optional but recommended] Inspect the trained autoencoder

python -m ae.test -f logs/dataset-test/ -ae logs/ae-32_000000.pkl --n-samples 50 -augment

## Use the AutoEncoder with a Gym wrapper

The Gym wrapper is `ae.wrapper.AutoencoderWrapper`, you can add it to the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo) (branch "offline-rl").

Export path to trained autoencoder

export AAE_PATH=/absolute/path/to/autoencoder.pkl

Then you can call python train.py --algo ... --env ... with the RL Zoo