Status: Maintenance (expect bug fixes and additional algorithms)
Weg's RL Baselines is a set of minimum viable implementations of reinforcement learning algorithms.
Most common implementations are overly complex or require more intimate knowledge of a companies internal software pipeline or frameworks. This imposes a real barrier to education in the field of reinforcement learning, where complexity of the algorithms is already a concern on its own.
Anything that would expand the files beyong minimum complexity is not included. Anything that would be considered boilerplate is not included. No model saving, loading, or testing. You can easily add those features yourself. Itertools and less common tensor operations, numpy, and pytorch features are avoided to make the baselines accessable to the average python programmer.
These algorithms function as a starting point for your own implementations. While not distributed implementations, they could be used in production with minimal modification. These implementations include the most commonly used drl algorithms, and if hyperparameter tuned, can achieve state of the art results in non-reward-sparse continuous control environments.
Baselines requires python3, pytorch, and openai-gym.
pip3 install gymThis may also be necessary:
pip3 install gym[box2d]- Clone the repo and cd into it:
git clone https://github.com/wegfawefgawefg/wegs-drl-baselines.git cd wegs-drl-baselines
Most of the algorithms in wegs-baselines repo are used by running the corresponding algorithm file:
python3 ./dqn.pyWeg's DRL Baselines does not include model saving or loading, as it goes against the intention of the repo to be minimal. However, adding this functionality is trivial.
You can store rewards and losses in a list and shove them into matplotlib or tensorboard.
These are complete and functioning, but are not recommended for use in making good quality RL bots.