单个智能体学习并共享模型，让团队自动分化出不同角色，无需通信。
Emergence of Roles in Robotic Teams with Model Sharing and Limited Communication
- 中心化学习+周期性模型共享，降低计算能耗。
- 无显式指令下实现角色分化，性能接近传统多智能体强化学习。
- 适合资源受限场景，如物流、环境监测等实际应用。
我们提出一种多智能体觅食系统中的强化学习策略：由单一智能体进行集中式学习，并定期将其模型分发给不参与学习的其他智能体。与常见的多智能体强化学习（MARL）和集中式学习模型相比，该方法显著降低了计算与能耗需求。通过设计促进角色发展的奖励函数，系统在无显式指令的情况下实现了智能体行为的差异化。这种隐式角色分化使智能体能根据与环境的交互动态调整自身角色，无需智能体间直接通信。该方法可有效提升觅食效率，具备向物流、环境监测及自主探索等真实场景转化的潜力。
原文摘要 · Abstract (English)
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.
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