Using DeepSeek AI to create an AI for a card game

AI is evolving rapidly. We have World Chess and Go champions that have been defeated by AI. Yet, trading card games like Magic the Gathering and Yu-Gi-Oh still have yet to make an AI that can do this. This is because of the complexity of a card game that has so many different types of cards that do different things and the randomness factor which makes each game different from its past. This is unlike Go and Chess where all pieces are always static at the start of the game and have the same function.

We have a lot of data of a lot of games from several thousand players using the best decks. This data is in the form of a json that has every move every turn, the action, the card that is used and what happened. We want to be able to see there is a way to train an AI to write an AI script using this data for a specific deck of cards and that deck's duels.

If interested, contact me. I don't have the GPU or computing power to do this myself.

Start by reviewing the repository's Python code and the issue's described JSON game-data format. Determine whether the project has an entry point or existing component for training an agent, and define a concrete scope and success criteria before implementation; no file or test is named in the issue.

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