Update README.md

README.md

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-## Introduction
-CropGym is a highly configurable [Python Open AI gym](https://gym.openai.com/) environment to conduct Reinforcement Learning (RL) research for crop management. CropGym is built around [PCSE](https://pcse.readthedocs.io/en/stable/), a well established python library that includes implementations of a variety of crop simulation models. CropGym follows standard gym conventions and enables daily interactions between an RL agent and a crop model.
-
-## Code 
-Source code is available at [https://github.com/BigDataWUR/PCSE-Gym](https://github.com/BigDataWUR/PCSE-Gym).
-
-## Installation instructions
-To install a minimalistic version, do the following:
-
-1. Clone [PCSE](https://github.com/ajwdewit/pcse.git)
-2. Clone [CropGym](https://github.com/BigDataWUR/PCSE-Gym)
-
-The code has been tested using python 3.8.10.
-## Example
-- [Basic example](tutorials/basic.md)
-- [Advanced example](tutorials/customization.md)
-
-## Use case
-A use case is built for nitrogen fertilization in winter wheat. A Jupiter notebook, showing a trained RL agent in action can be found [here](https://colab.research.google.com/github/BigDataWUR/PCSE-Gym/blob/master/notebooks/nitrogen-winterwheat/results_paper.ipynb).
+CropGym is a highly configurable [Python Open AI gym](https://gym.openai.com/) environment to conduct Reinforcement Learning (RL) research for crop management. CropGym is built around [PCSE](https://pcse.readthedocs.io/en/stable/), a well established python library that includes implementations of a variety of crop simulation models. Please refer to https://cropgym.ai/ for further information.