TianyangGuo / aae-train-donkeycar

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

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 (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