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+++ b/README.md
@@ -1,12 +1,14 @@
 # BSK-RL: Environments for Spacecraft Planning and Scheduling
 
-<img align="left" src="./docs/source/_images/static/bsk_rl-logo.png" width=200px style="margin:16px;">
+<img align="left" src="./docs/source/_images/static/bsk_rl-logo.png" width=180px style="margin:10px;">
 
 
-[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](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/).