Reiforcement Learning: Behaviors and Applications.

This library contains the companion code to the "Reinforcement Learning: Behaviors and Applications" course at Stanford University. It include a clean RL formulation and interface implementation and some example agents and environments.

Getting started

The easiest way to get started is by working with examples found in the examples subdirectory. A simple entry point is our tutorial notebook

Installation

The library is tested on Python 3.7, 3.8 and 3.9.

1. While you can install RLBA in your standard python environment, we strongly recommend using a Python virtual environment to manage your dependencies.

   python3 -m venv rlba
   source rlba/bin/activate
   pip install --upgrade pip setuptools wheel

2. Clone the RLBA GitHub repository

   git clone ssh://github.com/mibrahimi/rlba

3. Execute the following command from the main directory (where setup.py is located):

   pip install .[jax,testing]

References

The library uses some of the code, tools, and utilities from acme.