Update facebookresearch README.

README.md

@@ -3,22 +3,8 @@
 --------------------------------------------------------------------------------
 
 <p align="center">
-  <a href="https://circleci.com/gh/facebookresearch/nle">
-    <img src="https://circleci.com/gh/facebookresearch/nle.svg?style=shield" />
-  </a>
-  <a href="https://github.com/facebookresearch/nle/actions/workflows/build_docker.yml">
-    <img src="https://github.com/facebookresearch/nle/actions/workflows/test_and_deploy.yml/badge.svg?branch=main" />
-  </a>
-  <a href="https://pypi.python.org/pypi/nle/">
-    <img src="https://img.shields.io/pypi/v/nle.svg" />
-  </a>
-   <a href="https://pepy.tech/project/nle">
-    <img src="https://static.pepy.tech/personalized-badge/nle?period=total&units=international_system&left_color=black&right_color=orange&left_text=Downloads" />
-  </a>
-   <a href="https://twitter.com/NetHack_LE">
-    <img src="https://img.shields.io/twitter/follow/NetHack_LE?label=Twitter&style=social" alt="Twitter" />
-  </a>
- </p>
+  <b>Please find NLE at its new home at <a href="https://github.com/heiner/nle">github.com/heiner/nle</a></b>
+</p>
 
 The NetHack Learning Environment (NLE) is a Reinforcement Learning environment presented at [NeurIPS 2020](https://neurips.cc/Conferences/2020).
 NLE is based on [NetHack 3.6.6](https://github.com/NetHack/NetHack/tree/NetHack-3.6.6_PostRelease) and designed to provide a standard RL interface to the game, and comes with tasks that function as a first step to evaluate agents on this new environment.
@@ -277,4 +263,4 @@ If you use NLD or the datasets in any of your work, please cite:
   pages={24864--24878},
   year={2022}
 }
-```
+```