docs

Final_evaluation.md

@@ -11,12 +11,12 @@ NuGet Packages used and requeired for the project to work ar as follows:
 In our project agent (tractor) moves on resizable grid, which starting size is dependant on primary screen resolution.
 The task of the agent is to go through all soil tiles and plant different types of crops, use proper fertilizer and collect crops when fully grown.
 Apart from Machine Learning Algorithms used in project there are also many different features implemented like:
--Using A* algorithm to find an optimal path to previously selected target.
--Target is found by scoring system which assign score to a tile based on few factors like production rate or distance.
--Dynamically allocated cargo space for each fertilizer based on how often each fertilizer is used.
--Day and night cycle and season system.
--Using noise map generated for rainfall calculations to draw moving clouds.
--... and few other.
+ -Using A* algorithm to find an optimal path to previously selected target.
+ -Target is found by scoring system which assign score to a tile based on few factors like production rate or distance.
+ -Dynamically allocated cargo space for each fertilizer based on how often each fertilizer is used.
+ -Day and night cycle and season system.
+ -Using noise map generated for rainfall calculations to draw moving clouds.
+ -... and few other.
 
 ## Machine Learning Algorithms
 Project in its current state uses Machine Learning Algorithms to solve 2 problems. Light Gradient-Boosted Trees are used for both problems:
@@ -26,8 +26,11 @@ Project in its current state uses Machine Learning Algorithms to solve 2 problem
 	Then once a day AI is used to calculate base production rate multiplier. This part was done by Joel Städe.
 	
 ## Examples
+
+### Clouds
 ![Clouds](https://git.wmi.amu.edu.pl/s425077/PotatoPlan/raw/Joel-ML/Evaluation_examples/Example_clouds.PNG)
 
+### UI
 ![UI](https://git.wmi.amu.edu.pl/s425077/PotatoPlan/raw/Joel-ML/Evaluation_examples/Example_UI.PNG)
 
 Final evaluation doc.