带传感器约束的类车机器人追逃问题
Pursuit-Evasion for Car-like Robots with Sensor Constraints
AI总结：
针对带传感器约束的类车机器人追逃博弈的部分可观测难题，提出将历史信息编码为信念状态的学习方法，在捕获率上优于基线，可扩展博弈论求解器，且能在实体机器人上部署。
摘要：
我们将具有类车动力学特性和感知限制的双方追逃博弈形式化为部分可观测随机零和博弈，并对其展开研究。由感知约束导致的部分可观测性极具挑战性。例如，在智能体彼此不可见的场景下，它们需要从自身的传感器覆盖历史中提取信息，以推断对手的潜在位置。然而，保留历史信息会极大地增加状态空间的规模。为缓解此类部分可观测问题面临的挑战，我们提出了一种基于学习的新方法，该方法将历史信息编码为信念状态，并利用该状态生成智能体动作。实验结果表明，在追捕者的捕获率指标上，我们学习得到的策略相比现有多智能体强化学习基线最高提升了16%。此外，我们的实验结果显示，学习得到的信念状态是性能优异的状态估计器，可用于扩展现有博弈论求解器，同时我们证明了在现有完全可观测博弈论求解器计算可行的问题中，所提方法具备竞争力。最后，我们将学习到的策略部署在实体机器人上，在F1TENTH与JetRacer平台间开展博弈实验，机器人在室内环境中的最高移动速度达2 m/s，验证了这些策略可在真实机器人上运行。
英文摘要：
We study a pursuit-evasion game between two players with car-like dynamics and sensing limitations by formalizing it as a partially observable stochastic zero-sum game. The partial observability caused by the sensing constraints is particularly challenging. As an example, in a situation where the agents have no visibility of each other, they would need to extract information from their sensor coverage history to reason about potential locations of their opponents. However, keeping historical information greatly increases the size of the state space. To mitigate the challenges encountered with such partially observable problems, we develop a new learning-based method that encodes historical information to a belief state and uses it to generate agent actions. Through experiments we show that the learned strategies improve over existing multi-agent RL baselines by up to 16 % in terms of capture rate for the pursuer. Additionally, we present experimental results showing that learned belief states are strong state estimators for extending existing game theory solvers and demonstrate our method's competitiveness for problems where existing fully observable game theory solvers are computationally feasible. Finally, we deploy the learned policies on physical robots for a game between the F1TENTH and JetRacer platforms moving as fast as $\textbf{2 m/s}$ in indoor environments, showing that they can be executed on real-robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
