Using AI Agents to solve Card Game balance
Step 1 : We train the AI agent using Deep Reinforcement Learning algorithms.
Step 2 : The AI Agent plays against itself over and over again. This generates a large quantity of statistical data.
Step 3 : We use the generated statistics to identify imbalance and update the rules of the game.
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This week started off with the softs opening on Monday which was received well. We handed out our playable prototype to the faculty but because of the lengthy nature of the entire process of using our app, no one really playtested the application. However, we were able to show a…
Week 14 : Final Bug Fixes
This week involved more polishing of our app in order to prepare for softs. We identified several bugs through the course of last which needed to be ironed out. We also added some minor features aimed at improving user experience. Last but not the least, we also worked towards creating…
Week 13 : Preparation for Softs
This week saw refinement of features in our app. We playtested with a CMU faculty for three consecutive days to get a good understanding of how our app feels and if the data it offers is valuable. We got some good suggestions and have kept working on those suggestions to…
Week 12 : Polishing
This week was focused all on integrating the AI into the app and making sure that the one-click-training works correctly. We also focused on creating a new version of the app with reduced complexity as a result of faculty feedback we received indicating that our app can feel a little…
Week 11 : All About Integration