Issue #17: temp

CITATION.cff

@@ -0,0 +1,19 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Stephenson"
+  given-names: "Mark"
+  # orcid: "https://orcid.org/0000-0000-0000-0000"
+- family-names: "Mantovani"
+  given-names: "Lorenzzo"
+  # orcid: "https://orcid.org/0000-0000-0000-0000"
+- family-names: "Herrmann"
+  given-names: "Adam"
+  # orcid: "https://orcid.org/0000-0000-0000-0000"
+- family-names: "Schaub"
+  given-names: "Hanspeter"
+  # orcid: "https://orcid.org/0000-0000-0000-0000"
+title: "BSK-RL"
+version: 0.0.0
+date-released: 2023
+url: "https://github.com/AVSLab/bsk_rl/"

docs/source/citation.rst

@@ -0,0 +1,3 @@
+Citation
+========
+If you use this code in your research, please cite the repository as follows:

docs/source/conf.py

@@ -16,7 +16,7 @@
 # sys.path.insert(0, os.path.abspath(os.path.join("..", "..", "src")))
 now = datetime.datetime.now()
 
-project = "bsk_rl"
+project = "BSK-RL"
 copyright = str(now.year) + ", Autonomous Vehicle Systems (AVS) Laboratory"
 author = "Mark Stephenson"
 release = "0.0.0"

docs/source/index.rst

@@ -1,23 +1,63 @@
-Welcome to bsk_rl's documentation!
-==================================
+BSK-RL: Environments and Algorithms for Spacecraft Planning and Scheduling
+==========================================================================
 
-`bsk-rl` is a Python package consisting of various `Gymnasium <https://gymnasium.farama.org/index.html>`_ environments, agents, training scripts, and examples for spacecraft planning and scheduling problems, with an emphasis on reinforcement learning.
+.. note::
+    BSK-RL and its documentation are under active development.
 
-Installation instruction can be found at :doc:`install`.
 
-New environments should be based on :ref:`bsk_rl.envs.general_satellite_tasking`.
+``BSK-RL`` is a Python package consisting of various `Gymnasium <https://gymnasium.farama.org/index.html>`_ environments, agents, training scripts, and examples for spacecraft planning and scheduling problems, with an emphasis on reinforcement learning.
 
+New environments should be built using the :ref:`general satellite tasking framework <bsk_rl.envs.general_satellite_tasking>`.
+
+
+Quickstart
+----------
+To install BSK-RL:
+
+#. Install `Basilisk <https://hanspeterschaub.info/basilisk>`_, a spacecraft simulation framework.
+#. Clone BSK-RL.
+
+   .. code-block:: console
+
+      $ git clone git@github.com:AVSLab/bsk_rl.git && cd bsk_rl
+
+#. Install BSK-RL in the same virtual environment as Basilisk.
+
+   .. code-block:: console
+
+      (.venv) $ python -m pip install -e . && finish_install
+
+#. Test the installation.
+
+   .. code-block:: console
+
+      (.venv) $ pytest ./tests/examples
+
+Complete installation instructions and common troubleshooting tips can be found :doc:`here <install>`.
+
+
+Contents
+--------
 .. toctree::
    :maxdepth: 2
    :titlesonly:
 
    install
    API Reference/index
    Examples/index
+   publications
+   citation
+   GitHub <https://github.com/AVSLab/bsk_rl/>
 
 
 .. Indices
 .. =======
 
 .. * :ref:`genindex`
 .. * :ref:`modindex`
+
+Acknowledgements
+----------------
+This work has been supported by NASA Space Technology Graduate Research Opportunity (NSTGRO) grants, 80NSSC20K1162 and 80NSSC23K1182.
+
+This work has also been supported by Air Force Research Lab grant FA9453-22-2-0050.

docs/source/publications.rst

@@ -0,0 +1,7 @@
+Publications
+============
+The following publications have been produced using BSK-RL and its predecessors. If you have used BSK-RL in your research, please let us know and we will add your publication to the list.
+
+* A. Herrmann and H. Schaub, “Monte Carlo Tree Search With Value Networks For Autonomous Spacecraft Operations,” AAS/AIAA Astrodynamics Specialist Conference, Lake Tahoe, CA, Aug. 9–13, 2020.
+* A. Herrmann and H. Schaub, “Autonomous Spacecraft Tasking using Monte Carlo Tree Search Methods,” AAS/AIAA Space Flight Mechanics Meeting, Charlotte, NC, January 31–February 4, 2021. 
+* A. Herrmann, J. Vaz Carneiro and H. Schaub, “Reinforcement Learning For The Multi-Satellite Earth-Observing Scheduling Problem,” AAS Guidance and Control Conference, Breckenridge, CO, Feb. 3–9, 2022.