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