基于知情先验策略的有限理性无人机协调
Coordination of Bounded Rational Drones through Informed Prior Policy
AI总结：
本研究基于有限理性（BR）范式，设计了一种结合强化学习与环境探索噪声的知情先验策略，并将其整合至博弈论框架中，实现了多无人机在考虑计算约束下的高效协同运动规划与决策。
摘要：
生物智能体（如人类和动物）能够在有限时间内从大量选择中做出决策。它们能做到这一点，是因为它们利用先验知识找到的解不一定是最优的，但足以完成给定任务。在本研究中，我们基于上述有限理性（Bounded Rationality, BR）范式，研究多无人机在BR范式下的运动协调，以实现协同运动规划任务。具体而言，我们设计了一种先验策略，该策略在熟悉环境中提供有用的目标导向导航启发式信息，并通过增强环境相关探索噪声的强化学习在陌生环境中进行自适应。将此先验策略整合到博弈论有限理性框架中，使得智能体在群体中能够考虑其他智能体的计算约束并快速做出决策。我们的研究确保了具备充分知情先验策略的智能体能够提升群体集体决策能力的效率。我们在仿真和现实世界中进行了严格的实验，以证明知情智能体安全导航至目标的能力能够引导群体在BR框架下高效协调。
英文摘要：
Biological agents, such as humans and animals, are capable of making decisions out of a very large number of choices in a limited time. They can do so because they use their prior knowledge to find a solution that is not necessarily optimal but good enough for the given task. In this work, we study the motion coordination of multiple drones under the above-mentioned paradigm, Bounded Rationality (BR), to achieve cooperative motion planning tasks. Specifically, we design a prior policy that provides useful goal-directed navigation heuristics in familiar environments and is adaptive in unfamiliar ones via Reinforcement Learning augmented with an environment-dependent exploration noise. Integrating this prior policy in the game-theoretic bounded rationality framework allows agents to quickly make decisions in a group considering other agents' computational constraints. Our investigation assures that agents with a well-informed prior policy increase the efficiency of the collective decision-making capability of the group. We have conducted rigorous experiments in simulation and in the real world to demonstrate that the ability of informed agents to navigate to the goal safely can guide the group to coordinate efficiently under the BR framework.
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