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https://github.com/RLE-Foundation/RLeXplore
RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
https://github.com/RLE-Foundation/RLeXplore
baselines efficient-algorithm exploration-strategy gym machine-learning pybullet pytorch reinforcement-learning robotics toolbox
Last synced: 2 months ago
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RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
- Host: GitHub
- URL: https://github.com/RLE-Foundation/RLeXplore
- Owner: RLE-Foundation
- License: mit
- Created: 2022-09-19T11:38:16.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-29T22:51:34.000Z (3 months ago)
- Last Synced: 2024-02-29T23:26:25.486Z (3 months ago)
- Topics: baselines, efficient-algorithm, exploration-strategy, gym, machine-learning, pybullet, pytorch, reinforcement-learning, robotics, toolbox
- Language: Python
- Homepage: https://docs.rllte.dev/
- Size: 305 KB
- Stars: 304
- Watchers: 3
- Forks: 13
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-production-machine-learning - RLeXplore - exploration-baselines.svg?style=social) - RLeXplore provides stable baselines of exploration methods in reinforcement learning. (Industry Strength Benchmarking and Evaluation)
README
# RLeXplore: Benchmarking Intrinsic Reward Shaping in Reinforcement Learning
**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:
# Installation
- with pip `recommended`
Open a terminal and install **rllte** with `pip`:
``` shell
pip install rllte-core
```
- with git
Open a terminal and clone the repository from [GitHub](https://github.com/RLE-Foundation/rllte) with `git`:
``` sh
git clone https://github.com/RLE-Foundation/rllte.git
```
After that, run the following command to install package and dependencies:
``` sh
pip install -e .
```
Finally, you can invoke the intrinsic reward module by:
``` python
from rllte.xplore.reward import ICM, RIDE, ...
```
## Module List
| **Type** | **Modules** |
|--- |--- |
| Count-based | [PseudoCounts](https://arxiv.org/pdf/2002.06038), [RND](https://arxiv.org/pdf/1810.12894.pdf), [E3B](https://proceedings.neurips.cc/paper_files/paper/2022/file/f4f79698d48bdc1a6dec20583724182b-Paper-Conference.pdf) |
| Curiosity-driven | [ICM](http://proceedings.mlr.press/v70/pathak17a/pathak17a.pdf), [Disagreement](https://arxiv.org/pdf/1906.04161.pdf), [RIDE](https://arxiv.org/pdf/2002.12292) |
| Memory-based | [NGU](https://arxiv.org/pdf/2002.06038) |
| Information theory-based | [RE3](http://proceedings.mlr.press/v139/seo21a/seo21a.pdf) |
## Tutorials
Click the following links to get the code notebook:
0. [Quick Start](./0%20quick_start.ipynb)
1. [RLeXplore with RLLTE](./1%20rlexplore_with_rllte.ipynb)
2. [RLeXplore with Stable-Baselines3](./2%20rlexplore_with_sb3.ipynb)
3. [RLeXplore with CleanRL](./3%20rlexplore_with_cleanrl.py)
4. [Custom Intrinsic Rewards]()