forked from s425077/PotatoPlan
37 lines
2.0 KiB
Markdown
37 lines
2.0 KiB
Markdown
# Final Evaluation
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## Introduction
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PotatoPlan is an Inteligent Tractor AI Project and is written in C# using Monogame framework.
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NuGet Packages used and requeired for the project to work ar as follows:
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C5
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Microsoft.ML
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Microsoft.ML.LightGBM
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System.Drawing.Common
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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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## 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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1. Choosing a proper fertilizer which should be applied to current tile, based on few variables like nutrients in soil.
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Applying proper fertilizer boosts production rate of a crop (rate of growth of a crop). This part was done by Oskar Nastały.
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2. Calculating production rate of a tile based on rainfall and few other variabels. Noise map is generated and used to simulate dynamically changing rainfall.
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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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### UI
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Final evaluation doc.
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WIP |