docs: improve package description (#260)

.github/workflows/stable_learning_control.yml

@@ -57,9 +57,6 @@ jobs:
       fail-fast: false # Run all matrix jobs
       matrix:
         python-version: [3.8, 3.9, "3.10"] # Supported python versions
-    # permissions:
-    #   contents: read
-    #   pull-requests: write # Needed for codeconv to write on pull requests
     steps:
       - name: Checkout stable-learning-control repository
         uses: actions/checkout@v3
@@ -83,4 +80,3 @@ jobs:
         uses: codecov/codecov-action@v3
         env: 
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-

README.md

@@ -3,32 +3,15 @@
 [![Baysian Learning Control CI](https://github.com/rickstaa/stable-learning-control/actions/workflows/stable_learning_control.yml/badge.svg)](https://github.com/rickstaa/stable-learning-control/actions/workflows/stable_learning_control.yml)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/rickstaa/stable-learning-control)](https://github.com/rickstaa/stable-learning-control/releases)
 [![Python 3](https://img.shields.io/badge/Python->=3.8-brightgreen)](https://www.python.org/)
-[![codecov](https://codecov.io/gh/rickstaa/stable-learning-control/branch/main/graph/badge.svg?token=RFM3OELQ3L)](https://codecov.io/gh/rickstaa/stable-learning-control)
+[![codecov](https://codecov.io/gh/rickstaa/stable-learning-control/branch/main/graph/badge.svg?token=4SAME74CJ7)](https://codecov.io/gh/rickstaa/stable-learning-control)
 [![Contributions](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CONTRIBUTING.md)
 
 ## Package Overview
 
-Welcome to the Stable Learning Control (SLC) framework! The Stable Learning Control framework enables you to automatically create, train and deploy various safe (stable and robust) Reinforcement Learning (RL) and Imitation learning (IL) control algorithms directly from real-world data. This framework is made up of four main modules:
+The Stable Learning Control (SLC) framework is a collection of robust Reinforcement Learning control algorithms designed to ensure stability. These algorithms are built upon the Lyapunov actor-critic architecture introduced by [Han et al. 2020](http://arxiv.org/abs/2004.14288). They guarantee stability and robustness by leveraging [Lyapunov stability theory](https://en.wikipedia.org/wiki/Lyapunov_stability). These algorithms are specifically tailored for use with [gymnasium environments](https://gymnasium.farama.org/) that feature a positive definite cost function. Several ready-to-use compatible environments can be found in the [stable-gym](https://github.com/rickstaa/stable-gym) package.
 
-*   [Modeling](./stable_learning_control/modeling): Module that uses state of the art System Identification and State Estimation techniques to create an [gymnasium environment](https://gymnasium.farama.org/) out of real data.
-*   [Control](./stable_learning_control/control): Module used to train several [Stable Learning Control](https://rickstaa.github.io/stable-learning-control/control/control.html) RL/IL agents on the built [gymnasium](https://gymnasium.farama.org/) environments.
-*   [Hardware](./stable_learning_control/hardware): Module that can be used to deploy the trained RL/IL agents onto the hardware of your choice.
-
-This framework follows a code structure similar to the [Spinningup](https://spinningup.openai.com/en/latest/) educational package. By doing this, we hope to make it easier for new researchers to get started with our Algorithms. If you are new to RL, you are therefore highly encouraged first to check out the SpinningUp documentation and play with before diving into our codebase. Our implementation sometimes deviates from the [Spinningup](https://spinningup.openai.com/en/latest/) version to increase code maintainability, extensibility and readability.
-
-## Clone the repository
-
-Since the repository contains several git submodules to use all the features, it needs to be cloned using the `--recurse-submodules` argument:
-
-```bash
-git clone --recurse-submodules https://github.com/rickstaa/stable-learning-control.git
-```
-
-If you already cloned the repository and forgot the `--recurse-submodule` argument you can pull the submodules using the following git command:
-
-```bash
-git submodule update --init --recursive
-```
+> **Note**
+> This framework follows a code structure similar to the [Spinningup](https://spinningup.openai.com/en/latest/) educational package. By doing this, we aim to make it easier for new researchers to start with our Algorithms. If you are new to RL, you are therefore highly encouraged to check out the SpinningUp documentation and play with it before diving into our codebase. Our implementation sometimes deviates from the [Spinningup](https://spinningup.openai.com/en/latest/) version to increase code maintainability, extensibility and readability.
 
 ## Installation and Usage
 

package.json

@@ -1,7 +1,7 @@
 {
   "name": "stable-learning-control",
   "version": "4.0.0",
-  "description": "A framework for learning stable reinforcement learning policies.",
+  "description": "A framework for training theoretically stable (and robust) Reinforcement Learning control algorithms.",
   "keywords": [
     "reinforcement-learning",
     "simulation",

pyproject.toml

@@ -11,7 +11,7 @@ authors = [
     {name = "Rick Staa", email = "[email protected]"}
 ]
 license = {file = "LICENSE"}
-description = "A package that contains several gymnasium environments with cost functions compatible with (stable) RL agents (i.e. positive definite)."
+description = "A framework for training theoretically stable (and robust) Reinforcement Learning control algorithms."
 keywords = [
     "reinforcement-learning",
     "simulation",