标题： 具有模型共享和有限通信的机器人团队中角色的出现 显示英文标题

标题： Emergence of Roles in Robotic Teams with Model Sharing and Limited Communication

摘要： 我们提出了一种强化学习策略，用于多智能体觅食系统，在该策略中，学习集中在单个智能体上，并且其模型会周期性地传播到非学习智能体群体中。在多智能体强化学习（MARL）是常见方法的领域中，这种方法旨在显著降低计算和能量需求，与MARL和集中式学习模型等方法相比。通过开发高性能的觅食智能体，这些方法可以转化为实际应用，如物流、环境监测和自主探索。奖励函数被纳入此方法中，以促进智能体之间的角色发展，而无需明确指示。这导致了智能体之间行为的差异化。隐含的角色分化鼓励使得智能体可以根据与环境的交互动态改变角色成为可能，而无需智能体之间进行显式通信。 显示英文摘要

摘要： We present a reinforcement learning strategy for use in multi-agent foraging systems in which the learning is centralised to a single agent and its model is periodically disseminated among the population of non-learning agents. In a domain where multi-agent reinforcement learning (MARL) is the common approach, this approach aims to significantly reduce the computational and energy demands compared to approaches such as MARL and centralised learning models. By developing high performing foraging agents, these approaches can be translated into real-world applications such as logistics, environmental monitoring, and autonomous exploration. A reward function was incorporated into this approach that promotes role development among agents, without explicit directives. This led to the differentiation of behaviours among the agents. The implicit encouragement of role differentiation allows for dynamic actions in which agents can alter roles dependent on their interactions with the environment without the need for explicit communication between agents.