The correct option is Reinforcement learning approach.

In this scenario the agent interacts with a turn based environment by choosing actions and only receives a score after a match or every 12 turns. That feedback is a delayed reward signal and the objective is to learn a policy that maximizes long term cumulative reward. This is exactly what this approach addresses through trial and error interaction, credit assignment for delayed outcomes, and policy optimization.

Self-supervised Learning focuses on creating predictive pretext tasks on unlabeled data to learn useful representations, such as predicting masked tokens or image patches. It does not involve an agent taking actions in an environment or optimizing behavior based on reward signals, so it does not fit this sequential decision problem.

Supervised Learning requires labeled examples that map inputs to correct outputs. In this case there are no action labels and the feedback is evaluative and delayed in the form of a score, which is not the right supervision for direct supervised training.

Unsupervised Learning discovers structure in data such as clusters or embeddings without any rewards or labels. It does not learn policies to act over time, so it is not appropriate here.

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