Old code below

• Vanilla PPO
• Encoder, MLP Transition + Reward Models
• Encoder, Conv Transition + Reward Models
• Encoder, Conv Transition + Factored Reward Models
• Encoder, Conv Transition + Factored Reward Models
• Graph Neural Networks via this paper and this code

This is a PyTorch implementation of the methods proposed in

Automatic Data Augmentation for Generalization in Deep Reinforcement Learning by

Roberta Raileanu, Max Goldstein, Denis Yarats, Ilya Kostrikov, and Rob Fergus.

The code was run on a GPU with CUDA 10.2. To install all the required dependencies:

conda create -n auto-drac python=3.7
conda activate auto-drac

git clone git@github.com:rraileanu/auto-drac.git
cd auto-drac
pip install -r requirements.txt

git clone https://github.com/openai/baselines.git
cd baselines 
python setup.py install 

pip install procgen

cd auto-drac

python train.py --env_name bigfish --aug_type crop

python train.py --env_name bigfish --use_ucb

python train.py --env_name bigfish --use_rl2

python train.py --env_name bigfish --use_meta

UCB-DrAC achieves state-of-the-art performance on the Procgen benchmark (easy mode), significantly improving the agent's generalization ability over standard RL methods such as PPO.

Test Results on Procgen

Train Results on Procgen

This code was based on an open sourced PyTorch implementation of PPO.

We also used kornia for some of the augmentations.