Impersonal rephrasing.

paper.md

@@ -80,7 +80,7 @@ The package also includes a second environment option. Similar to the first, thi
 
 The grid is divided into square cells, each representing a quadrant with sides measuring 130 meters in the real world. This correlation with real-world dimensions is crucial for developing agents capable of learning from realistic motion patterns. The drones, which are controlled by reinforcement learning algorithms, serve as these agents. During the environment's instantiation, users define the drones' nominal speeds. These drones can move both orthogonally and diagonally across the grid, and they are equipped to search each cell for the presence of the PIW.
 
-Several works have been developed over the past few years to define better algorithms for the search and rescue of shipwrecks [@AI2021110098; @WU2024116403]. However, no environment for agent training is made available publicly. For this reason, we believe that the development and provision of this environment as a Python library and open-source project will be relevant to the machine learning community and ocean safety.
+Several works have been developed over the past few years to define better algorithms for the search and rescue of shipwrecks [@AI2021110098; @WU2024116403]. However, no environment for agent training is made available publicly. For this reason, the development and provision of this environment as a Python library and open-source project are expected to have significant relevance to the machine learning community and ocean safety.
 
 This new library makes it possible to implement and evaluate new reinforcement learning algorithms, such as Deep Q-Networks (DQN) [@dqn2015] and Proximal Policy Optimization (PPO) [@ppo2017], with little effort. An earlier iteration of this software was utilized in research that compared the Reinforce algorithm with the parallel sweep path planning algorithm [@dsse2023].