Finish up section writing (?)

paper/paper.md

@@ -34,9 +34,9 @@ A major effect of climate change today is the increased frequency and intensity
 
 There has been significant traction in the use of computational methods to study wildfires. In particular, reinforcement learning -- a subdomain of artificial intelligence where models learn through interaction with their environment -- has seen growing interest from researchers. Applying reinforcement learning requires modeling the spread of wildfires. Traditionally, modeling was primarily done using physics-based methods [@rothermel1972mathematical; @Andrews_1986]. However, newer methods are more data-driven, enabling the use of a higher diversity of features [@https://doi.org/10.1002/eap.1898; @diao2020uncertainty].
 
-Researchers have recently been studying wildfire surveillance and monitoring. While various forms of machine learning (ML), such as computer vision [@ganapathi2018using], have been used to solve this task, the most popular method by far has been to employ reinforcement learning [@julian2019distributed; @altamimi2022large; @9340340].
+Researchers have recently been studying wildfire surveillance and monitoring. While various forms of machine learning, such as computer vision [@ganapathi2018using], have been used to solve this task, the most popular method by far has been to employ reinforcement learning [@julian2019distributed; @altamimi2022large; @9340340]. Research in surveillance and monitoring has been supported by open-source environments for modeling wildfire spread and surveillance [@cellular_automata; @forest_fire].
 
-There has also been interest in optimizing the evacuation process [@https://doi.org/10.1111/risa.12944]. However, there exists no significant literature on the application of computational methods to model and simulate evacuation. Subsequently, while there has been much work around open-source environments for modeling wildfire spread and surveillance [@cellular_automata; @forest_fire], none exist for the task of evacuation. We believe that by creating a generalizable environment for reinforcement learning, we can encourage more research in the realm of wildfire evacuation.
+There has also been emerging interest in optimizing the evacuation process using computational methods [@https://doi.org/10.1111/risa.12944]. However, no reinforcement learning environments exist for the task of evacuation. We hope that open-source that tools for evacuation will  spur development in this area.
 
 # Methods