  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.
