Coordination of Bounded Rational Drones through Informed Prior Policy

TL;DR

This paper presents a method for coordinating multiple drones using a prior policy based on bounded rationality, combining reinforcement learning and game theory to improve collective decision-making in familiar and unfamiliar environments.

Contribution

It introduces an adaptive prior policy for drones that enhances cooperative motion planning within a bounded rationality framework, integrating reinforcement learning and game theory.

Findings

Informed prior policies improve decision-making efficiency.

Drones navigate safely and coordinate effectively in simulations and real-world tests.

The approach adapts to environment familiarity, enhancing robustness.

Abstract

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.