In this paper, we present a method for finding approximate Nash equilibria in a broad class of reachability games. These games are often used to formulate both collision avoidance and goal satisfaction. Our method is computationally efficient, running in real-time for scenarios involving multiple players and more than ten state dimensions. The proposed approach forms a family of increasingly exact approximations to the original game. Our results characterize the quality of these approximations and show operation in a receding horizon, minimally-invasive control context. Additionally, as a special case, our method reduces to local gradient-based optimization in the single-player (optimal control) setting, for which a wide variety of efficient algorithms exist.
翻译:在本文中,我们展示了一种在一系列广泛的可达性游戏中找到近似 Nash 平衡的方法。 这些游戏常常用来制定避免碰撞和目标满意度。 我们的方法是计算效率高的,在涉及多个玩家和十多个州层面的场景中实时运行。 提议的方法形成了一个与原始游戏越来越精确近似的组合。 我们的结果体现了这些近似的质量,并显示了在一个退缩的视野中,最小侵入性控制环境下的操作。 此外,作为一个特殊的例子,我们的方法在单一玩家(最佳控制)设置中减少了以本地梯度为基础的优化,而对于单一玩家(最佳控制)设置中存在各种各样的高效算法。