Tags: adversarial* + machine learning*

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  1. "An example of simultaneously optimizing two policies for two adversarial agents, looking specifically at the cat and mouse game."

    The article explores developing strategies for two players with conflicting goals, using methods like game trees, reinforcement learning, and hill-climbing optimization. The focus is on determining optimal policies for each player to either catch or evade capture, considering board configurations and player turn orders. The article further details how hill climbing is applied to improve strategies incrementally, using variations in policies to evaluate and enhance performance over numerous iterations.

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