Reinforcement Learning in Duckietown

Outline

Work in progress repository, that contains various rl algorithms and classic control theory benchmarked in duckietown Current algortihms will be:

1. Classic PID.
2. Deep Deterministic Policy Gradient Algorithm and it's modifications. Currently implemented DDPG with VAE taken from https://github.com/araffin/learning-to-drive-in-5-minutes

Installation

1. Install https://github.com/Laboratory-of-Embodied-Intelligence/gym-duckietown with instructions in readme.
2. conda env create --name MY_ENV_NAME -f environment.yml
3. cd rl && python3 stable_train.py will train DDPG+VAE, cd rl && python3 train.py will train vanilla version of DDPG taken from https://github.com/philtabor/Deep-Q-Learning-Paper-To-Code

Training parameters

There are a lot of hyperparameters to choose, all list is written in stable_train.py, by tweaking them you can drastically change behaviour of the model.