f

.github/pull_request_template.md

@@ -19,7 +19,7 @@ Please describe the tests that you ran to verify your changes.
 ### Passes Tests
 - [ ] __Unit tests__ `pytest --cov bsk_rl --cov-report term-missing tests/unittest`
 - [ ] __Integrated tests__ `pytest --cov bsk_rl --cov-report term-missing tests/integration`
-- [ ] __Examples__ `pytest tests/examples`
+- [ ] __Documentation builds__ `cd docs; make html`
 
 ### Test Configuration
 - Python:

.github/workflows/documentation.yml

@@ -25,7 +25,7 @@ jobs:
           cp docs/sitecustomize.py $(python -c 'import site; print(site.getsitepackages()[0])')/sitecustomize.py
       - name: Install dependencies
         run: |
-          pip install -e .
+          pip install -e '.[docs]'
         # skip finish install steps
       - name: Sphinx build
         run: |

.github/workflows/documentation_refactor.yml

@@ -26,7 +26,7 @@ jobs:
           cp docs/sitecustomize.py $(python -c 'import site; print(site.getsitepackages()[0])')/sitecustomize.py
       - name: Install dependencies
         run: |
-          pip install -e .
+          pip install -e '.[docs,rllib]'
         # skip finish install steps
       - name: Sphinx build
         run: |

.isort.cfg

@@ -1,2 +1,2 @@
 [settings]
-profile=black
+profile=black

.ruff.toml

@@ -1 +1 @@
-
+extend-include = ["*.ipynb"]

README.md

@@ -1,7 +1,14 @@
-# BSK-RL: Environments and Algorithms for Spacecraft Planning and Scheduling
-[BSK-RL](https://avslab.github.io/bsk_rl/) ([Basilisk](https://hanspeterschaub.info/basilisk) + [Reinforcement Learning](https://en.wikipedia.org/wiki/Reinforcement_learning)) is a Python package for constructing [Gymnasium](https://gymnasium.farama.org/index.html) environments for spacecraft tasking problems. It is built on top of [Basilisk](https://hanspeterschaub.info/basilisk), a modular and fast spacecraft simulation framework, making the simulation environments high-fidelity and computationally efficient. BSK-RL also includes a collection of agents, training scripts, and examples for working with these environments.
+# BSK-RL: Environments for Spacecraft Planning and Scheduling
+
+<img align="right" src="./docs/source/_images/static/bsk_rl-logo.png" width=180px>
+
+
+[BSK-RL](https://avslab.github.io/bsk_rl/) ([Basilisk](https://hanspeterschaub.info/basilisk) + [Reinforcement Learning](https://en.wikipedia.org/wiki/Reinforcement_learning)) is a Python package for constructing [Gymnasium](https://gymnasium.farama.org/index.html) environments for spacecraft tasking problems. It is built on top of [Basilisk](https://hanspeterschaub.info/basilisk), a modular and fast spacecraft simulation framework, making the simulation environments high-fidelity and computationally efficient. 
+
+### Usage
+Installation instructions, examples, and documentation can be found on the [BSK-RL website](https://avslab.github.io/bsk_rl/).
+
+### Acknowledgment
 
 BSK-RL is developed by the [Autonomous Vehicle Systems (AVS) Lab](https://hanspeterschaub.info/AVSlab.html) at the University of Colorado Boulder.
 
-## Usage
-Installation instructions, examples, and documentation can be found on the [BSK-RL website](https://avslab.github.io/bsk_rl/) (under construction).