docs: update pypi readme

PYPI_README.rst

@@ -16,20 +16,54 @@
         :target: https://opensource.org/licenses/MIT
         :alt: License
 
-A toolbox for benchmarking reinforcement learning (RL) algorithms on building energy optimisation (BEO) problems. Beobench does not replace existing libraries defining BEO problems (such as `BOPTEST <https://github.com/ibpsa/project1-boptest>`_) — instead it makes working with them easier.
+A toolbox for benchmarking reinforcement learning (RL) algorithms on building energy optimisation (BEO) problems. Beobench tries to make working on RL for BEO easier: it provides simple access to existing libraries defining BEO problems (such as `BOPTEST <https://github.com/ibpsa/project1-boptest>`_) and provides a large set of pre-configured RL algorithms. Beobench is *not* a gym library itself - instead it leverages the brilliant work done by many existing gym-type projects and makes their work more easily accessible.
 
 Features
 --------
 
-- *Wide range of RL algorithms:* test the most common RL algorithms on BEO problems without re-implementing by using beobench's `Ray RLlib <https://github.com/ray-project/ray/tree/master/rllib>`_ integration.
-- *Experiment logging:* log experiment results in a reproducible and sharable manner via `Weights and Biases`_.
-- *Hyperparameter tuning:* easily tune hyperparameters using the extensive `Ray Tune syntax <https://docs.ray.io/en/master/tune/index.html>`_.
-- *Installers:* avoid having to manage messy Python namespaces yourself — just install beobench via pip and use its pre-configured docker containers to take care of managing other BEO packages and their dependencies.
+*Some of the features are work in progress*
+
+**Main features**
+
+- *RL algorithm collection:* what's the best RL method for your BEO problem? Building on `Ray RLlib <https://github.com/ray-project/ray/tree/master/rllib>`_, beobench provides a large collection of pre-configured RL algorithm experiments that can be easily applied to your new BEO problem.
+- *Problem collection:* beobench provides ready-to-use docker containers for popular BEO gym-type problem libraries. By enforcing a strict OpenAI ``gym.Env`` it makes testing your method on different libraries easy.
+
+**Additional features**
+
+- *Experiment logging:* log experiment results in a reproducible and shareable manner via `Weights and Biases`_.
+- *Hyperparameter tuning:* easily tune hyperparameters using the extensive `Ray Tune Search API <https://docs.ray.io/en/master/tune/index.html>`_.
+- *Simple installation:* beobench can be installed via pip and only requires docker as an additional non-python dependency.
+- *Easily extendable:* beobench is designed for the user to add both environments and methods.
 
 .. _Weights and Biases: https://wandb.ai/
 
 .. end-in-sphinx-docs
 
+
+.. start-quickstart
+
+Quickstart
+----------
+
+Run your first beobench experiment in three steps:
+
+1. `Install docker <https://docs.docker.com/get-docker/>`_ on your machine (if on Linux, check the `additional installation steps <https://beobench.readthedocs.io/en/latest/guides/installation_linux.html>`_)
+2. Install *beobench* using:
+
+        .. code-block:: console
+
+                pip install beobench
+
+3. Finally, start your first experiment using:
+
+        .. code-block:: console
+
+                python -m beobench.experiment.scheduler
+
+Done, you have just started your first experiment... congrats! Check out the `full getting started guide in the documentation <https://beobench.readthedocs.io/en/latest/guides/getting_started.html>`_ for the next steps.
+
+.. end-quickstart
+
 Documentation
 -------------
 https://beobench.readthedocs.io
@@ -47,3 +81,4 @@ This package was originally created with Cookiecutter_ and the `audreyr/cookiecu
 
 .. _Cookiecutter: https://github.com/audreyr/cookiecutter
 .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
+