Theresa Eimer: docs homepage

main/_sources/basic_usage/env_subsets.rst.txt

@@ -5,7 +5,7 @@ We analyzed the hyperparameter landscapes of PPO, DQN and SAC on 20 environments
 
 .. image:: path/subsets.png
   :width: 800
-  :alt: Alternative text
+  :alt: Environment subsets for PPO, DQN and SAC
 
 We strongly recommend you focus your benchmarking on these exact environments to ensure you cover the space total landscape of RL behaviors well. 
 The data generated for selecting these environments is available on `HuggingFace <https://huggingface.co/datasets/autorl-org/arlbench>`_ for you to use in your experiments.

main/_sources/index.rst.txt

@@ -15,4 +15,15 @@ Home
    contributing
 
 
-Lorem ipsum dolor.
+Welcome to ARLBench, your pathway into efficient AutoRL!
+We offer lightning-fast implementations of PPO, DQN and SAC that are highly configurable as well as a subset of environments which will give you a representative result of your AutoRL method across the RL landscape.
+This documentation, in combination with our examples, should provide you with a starting point to get your AutoRL method up and running in no time.
+
+.. image:: path/structure.png
+  :width: 800
+  :alt: ARLBench overview figure
+
+You will interact with ARLBench through the `AutoRL Environment` class, which is the main entry point for all the functionalities that ARLBench offers.
+It works similarly to a gymnasium environment: you can use 'reset' to start a fresh run and then 'step' to take one configuration step. 
+Between steps, you can change your configuration to allow for flexible schedules.
+Additionally, the AutoRL Environment can also return information about the RL algorithm's internal state so you can build and learn reactive schedules as well. 

main/basic_usage/env_subsets.html

@@ -302,7 +302,7 @@
   <section id="the-arlbench-subsets">
 <h1>The ARLBench Subsets<a class="headerlink" href="#the-arlbench-subsets" title="Link to this heading">¶</a></h1>
 <p>We analyzed the hyperparameter landscapes of PPO, DQN and SAC on 20 environments to select a subset which allows for efficient benchmarking of AutoRL algorithms. These are the resulting subsets:</p>
-<a class="reference internal image-reference" href="basic_usage/path/subsets.png"><img alt="Alternative text" src="basic_usage/path/subsets.png" style="width: 800px;" />
+<a class="reference internal image-reference" href="basic_usage/path/subsets.png"><img alt="Environment subsets for PPO, DQN and SAC" src="basic_usage/path/subsets.png" style="width: 800px;" />
 </a>
 <p>We strongly recommend you focus your benchmarking on these exact environments to ensure you cover the space total landscape of RL behaviors well.
 The data generated for selecting these environments is available on <a class="reference external" href="https://huggingface.co/datasets/autorl-org/arlbench">HuggingFace</a> for you to use in your experiments.

main/index.html

@@ -304,7 +304,15 @@
 <h1>Home<a class="headerlink" href="#home" title="Link to this heading">¶</a></h1>
 <div class="toctree-wrapper compound">
 </div>
-<p>Lorem ipsum dolor.</p>
+<p>Welcome to ARLBench, your pathway into efficient AutoRL!
+We offer lightning-fast implementations of PPO, DQN and SAC that are highly configurable as well as a subset of environments which will give you a representative result of your AutoRL method across the RL landscape.
+This documentation, in combination with our examples, should provide you with a starting point to get your AutoRL method up and running in no time.</p>
+<a class="reference internal image-reference" href="path/structure.png"><img alt="ARLBench overview figure" src="path/structure.png" style="width: 800px;" />
+</a>
+<p>You will interact with ARLBench through the <cite>AutoRL Environment</cite> class, which is the main entry point for all the functionalities that ARLBench offers.
+It works similarly to a gymnasium environment: you can use ‘reset’ to start a fresh run and then ‘step’ to take one configuration step.
+Between steps, you can change your configuration to allow for flexible schedules.
+Additionally, the AutoRL Environment can also return information about the RL algorithm’s internal state so you can build and learn reactive schedules as well.</p>
 </section>