Game theoretic methods have become popular for planning and prediction in situations involving rich multi-agent interactions. However, these methods often assume the existence of a single local Nash equilibria and are hence unable to handle uncertainty in the intentions of different agents. While maximum entropy (MaxEnt) dynamic games try to address this issue, practical approaches solve for MaxEnt Nash equilibria using linear-quadratic approximations which are restricted to unimodal responses and unsuitable for scenarios with multiple local Nash equilibria. By reformulating the problem as a POMDP, we propose MPOGames, a method for efficiently solving MaxEnt dynamic games that captures the interactions between local Nash equilibria. We show the importance of uncertainty-aware game theoretic methods via a two-agent merge case study. Finally, we prove the real-time capabilities of our approach with hardware experiments on a 1/10th scale car platform.

翻译：博弈论方法在涉及丰富多智能体交互的规划与预测场景中已变得普遍。然而，这些方法通常假设存在单一局部纳什均衡，因此无法处理不同智能体意图中的不确定性。尽管最大熵（MaxEnt）动态博弈试图解决这一问题，但实际方法采用线性二次近似求解MaxEnt纳什均衡，这种近似局限于单模态响应，不适用于存在多个局部纳什均衡的复杂场景。通过将问题重新表述为部分可观测马尔可夫决策过程（POMDP），我们提出MPOGames方法，用于高效求解能够捕捉局部纳什均衡间相互作用的MaxEnt动态博弈。通过双智能体并道案例研究，我们展示了不确定性感知博弈论方法的重要性。最后，我们通过在1/10比例车辆平台上的硬件实验证明了该方法的实时性能。