A fancy icon for fancy gym

README.md

@@ -1,13 +1,17 @@
-# Fancy Gym
+<h1 align="center">
+  <br>
+  <img src='./icon.svg' width="250px">
+  <br><br>
+  <b>Fancy Gym</b>
+  <br>
+  <br>
+</h1>
 
-`fancy_gym` offers a large variety of reinforcement learning environments under the unifying interface
-of [OpenAI gym](https://gymlibrary.dev/). We provide support (under the OpenAI gym interface) for the benchmark suites
-[DeepMind Control](https://deepmind.com/research/publications/2020/dm-control-Software-and-Tasks-for-Continuous-Control)
-(DMC) and [Metaworld](https://meta-world.github.io/). If those are not sufficient and you want to create your own custom
-gym environments, use [this guide](https://www.gymlibrary.dev/content/environment_creation/). We highly appreciate it, if
-you would then submit a PR for this environment to become part of `fancy_gym`.  
-In comparison to existing libraries, we additionally support to control agents with movement primitives, such as Dynamic
-Movement Primitives (DMPs) and Probabilistic Movement Primitives (ProMP).
+`fancy_gym` offers a large variety of reinforcement learning environments under the unifying interface of [Gymnasium](https://gymnasium.farama.org/).
+
+We provide support (under the Gymnasium interface) for the benchmark suites [DeepMind Control](https://deepmind.com/research/publications/2020/dm-control-Software-and-Tasks-for-Continuous-Control) (DMC) and [Metaworld](https://meta-world.github.io/). If those are not sufficient and you want to create your own custom gym environments, use [this guide] https://www.gymlibrary.dev/content/environment_creation/). We highly appreciate it, if you would then submit a PR for this environment to become part of `fancy_gym`.
+
+In comparison to existing libraries, we additionally support to control agents with movement primitives, such as Dynamic Movement Primitives (DMPs) and Probabilistic Movement Primitives (ProMP).
 
 ## Movement Primitive Environments (Episode-Based/Black-Box Environments)
 
@@ -73,7 +77,7 @@ for i in range(1000):
 
     if done:
         obs = env.reset()
-``` 
+```
 
 When using `dm_control` tasks we expect the `env_id` to be specified as `dmc:domain_name-task_name` or for manipulation
 tasks as `dmc:manipulation-environment_name`. For `metaworld` tasks, we require the structure `metaworld:env_id-v2`, our