seagul
This is my rifle. There are many like it, but this one is mine.
- Major General William H. Rupertus
A generic utility library for deep reinforcement learning. I built this throughout my PhD as sort of a personal swiss-army knife for my research. There are other libraries that do similar things, this one is mine.
Highlights include:
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Implementations of several deep reinforcement learning algorithms, including Proximal Policy Optimization, Augmented Random Search, and Soft Actor Critic. All using a unified interface.
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Neural network utilities, Including supervised learning functionality mimicking keras' model.fit in pytorch, and an interface to make various MLPs, RBFs etc that are compatible with the rest of this library.
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Tests Known good hyper parameters across different environements for the algorithms.
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Custom OpenAI gym environments, That I've studied at some point or another in my research.
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Experiment utilities, For saving RL experiments along with meta data, logs, and a mechanism to restore the trained agents.
Who should use this?
If you just want to get your feet wet with deep RL there are better places to start, I usually recomend stable-baselines3. Most of the value of something like this is that I personally know it inside and out, but that won't be an advantage to someone out.
If you are a student working with me, or someone looking to extend my previous work, you've come to the right place.
Installation
This is a python package, so you can install it with:
pip install git+https://github.com/sgillen/seagul
Or if you want to make changes:
git clone https://github.com/sgillen/seagul
cd seagul
pip install -e .
I think that the tests folder linked above is a good starting point, and many files have a main restricted portion that shows some basic usage.