To optimally coordinate with others in cooperative games, it is often crucial to have information about one's collaborators: successful driving requires understanding which side of the road to drive on. However, not every feature of collaborators is strategically relevant: the fine-grained acceleration of drivers may be ignored while maintaining optimal coordination. We show that there is a well-defined dichotomy between strategically relevant and irrelevant information. Moreover, we show that, in dynamic games, this dichotomy has a compact representation that can be efficiently computed via a Bellman backup operator. We apply this algorithm to analyze the strategically relevant information for tasks in both a standard and a partially observable version of the Overcooked environment. Theoretical and empirical results show that our algorithms are significantly more efficient than baselines. Videos are available at https://minknowledge.github.io.

翻译：在合作博弈中，为了与他人实现最优协调，通常必须掌握有关协作者的信息：成功驾驶需要了解在道路的哪一侧行驶。然而，并非协作者的每个特征都具有战略相关性：在保持最优协调的同时，可以忽略驾驶员精细的加速度信息。我们表明，战略相关与不相关信息之间存在一个明确的分界。此外，我们证明，在动态博弈中，这种分界具有一个紧凑的表示，并可通过贝尔曼回溯算子高效计算。我们将该算法应用于分析Overcooked环境（包括标准版本和部分可观测版本）中任务所需的战略相关信息。理论和实验结果表明，我们的算法显著优于基线方法。视频可访问 https://minknowledge.github.io 获取。