RLeXplore is a unified, highly-modularized and plug-and-play toolkit that currently provides high-quality and reliable implementations of eight representative intrinsic reward algorithms. It used to be challenging to compare intrinsic reward algorithms due to various confounding factors, including distinct implementations, optimization strategies, and evaluation methodologies. Therefore, RLeXplore is designed to provide unified and standardized procedures for constructing, computing, and optimizing intrinsic reward modules.
The workflow of RLeXplore is illustrated as follows:
- with pip
recommended
Open a terminal and install rllte with pip:
pip install rllte-core- with git
Open a terminal and clone the repository from GitHub with git:
git clone https://github.com/RLE-Foundation/rllte.gitAfter that, run the following command to install package and dependencies:
pip install -e . Finally, you can invoke the intrinsic reward module by:
from rllte.xplore.reward import ICM, RIDE, ...| Type | Modules |
|---|---|
| Count-based | PseudoCounts, RND, E3B |
| Curiosity-driven | ICM, Disagreement, RIDE |
| Memory-based | NGU |
| Information theory-based | RE3 |
Click the following links to get the code notebook:
