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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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).
| Date | Stars |
|---|---|
| 2026-07-31 | 466 |
| 2026-08-01 | 466 |
| 2026-08-02 | 466 |
| 2026-08-05 | 465 |
| 2026-08-06 | 465 |
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<div align=center> <br> <img src='./assets/logo.png' style="width: 70%"> <br> ## RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning </div> **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: <div align=center> <img src='./assets/workflow.png' style="width: 100%"> </div> # Table of Contents - [Installation](#installation) - [Module List](#module-list) - [Tutorials](#tutorials) - [Benchmark Results](#benchmark-results) - [Cite Us](#cite-us) # Installation - with pip `recommended` Open a terminal and install **rllte** with `pip`: ``` shell conda create -n rllte python=3.8 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 pip install -e . ``` Now 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. [Exploring Hybrid Intrinsic Rewards](./4%20hybrid_intrinsic_rewards.ipynb) 4. [Custom Intrinsic Rewards](./5%20custom_intrinsic_reward.ipynb) ## Benchmark Results We have published a space using Weights & Biases (W&B) to store reusable experiment results on recognized benchmarks. The space link is: [RLeXplore's W&B Space](https://wandb.ai/yuanmingqi/RLeXplore/reportlist). <div align=center> <img src='./assets/wandb.png' style="width: 75%"> </div> - `RLLTE's PPO+RLeXplore` on *SuperMarioBros*: <div align=center> <img src='./assets/smb.png' style="width: 100%"> </div> - `RLLTE's PPO+RLeXplore` on *MiniGrid*: + DoorKey-16×16 <div align=center> <img src='./assets/mgd.png' style="width: 100%"> </div> + KeyCorridorS8R5, KeyCorridorS9R6, KeyCorridorS10R7, MultiRoom-N7-S8, MultiRoom-N10-S10, MultiRoom-N12-S10, Dynamic-Obstacles-16x16, and LockedRoom <div align=center> <img src='./assets/mg_hard.png' style="width: 100%"> </div> - `RLLTE's PPO+RLeXplore` on *Procgen-Maze*: + Number of levels=1 <div align=center> <img src='./assets/procgen_1maze.png' style="width: 100%"> </div> + Number of levels=200 <div align=center> <img src='./assets/procgen_allmaze.png' style="width: 100%"> </div> - `RLLTE's PPO+RLeXplore` on five hard-exploration tasks of *ALE*: | **Algorithm** | **Gravitar** | **MontezumaRevenge** | **PrivateEye** | **Seaquest** | **Venture** | |:-------------:|:------------:|:--------------------:|:--------------:|:------------:|:-----------:| | Extrinsic | **1060.19** | 42.83 | 88
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cc191917f14d9b1d, llm:Repository description and topics: 'stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and RIDE'; topics include reinforcement-learning, robotics, pytorch, baselines, exploration-strategy.
matched fp:cc191917f14d9b1d, llm:Repository description and topics: 'stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and RIDE'; topics include reinforcement-learning, robotics, pytorch, baselines, exploration-strategy.
matched fp:cc191917f14d9b1d, llm:Repository description and topics: 'stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and RIDE'; topics include reinforcement-learning, robotics, pytorch, baselines, exploration-strategy.