/Reinforcement-Learning-in-a-nutshell

A high-level preview over main concepts of RL and its applications

Primary LanguageJupyter Notebook

Reinforcement Learning in a nutshell

A high-level preview over main concepts of RL and its applications using Gym environments and stable_baselines3

GYM

Gym is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Since its release, Gym's API has become the field standard for doing this.

Gym documentation website is at https://www.gymlibrary.ml/, and you can propose fixes and changes to it here.

Stable Baseline3

Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines.

Github repository: https://github.com/DLR-RM/stable-baselines3

Paper: https://jmlr.org/papers/volume22/20-1364/20-1364.pdf

Main Features¶

Unified structure for all algorithms
PEP8 compliant (unified code style)
Documented functions and classes
Tests, high code coverage and type hints
Clean code
Tensorboard support
The performance of each algorithm was tested (see Results section in their respective page)