Dungeon Crawl AI Agent Optimization via Machine Learning

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For my senior project, I’m working on optimizing the performance of qw, an AI Agent developed to play (and sometimes win) the game of Dungeon Crawl Stone Soup.

To reduce the search space and make optimization with machine learning faster, easier, and more efficient, I’m limiting the bot to only explore the first floor of the Dungeon. This takes significantly faster to complete compared to running through a full game, and the AI agent will be faced with a much smaller set of monsters, items and dungeon features.

Currently, the plan is to adjust minor variables in the coding of qw to see if it can survive the first floor of the dungeon with a higher rate of success.

Following is my experimental design.

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