Recent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning have made human-AI collaboration easier for humans to team with AI agents. Leveraging human expertise and experience with AI in intelligent systems can be efficient and beneficial. Still, it is unclear to what extent human-AI collaboration will be successful, and how such teaming performs compared to humans or AI agents only. In this work, we show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents in a complex simulation environment. In addition, we have developed a new simulator for critical infrastructure protection, focusing on a scenario where AI-powered drones and human teams collaborate to defend an airport against enemy drone attacks. We develop a user interface to allow humans to assist AI agents effectively. We demonstrated that agents learn faster while learning from policy correction compared to learning from humans or agents. Furthermore, human-AI collaboration requires lower mental and temporal demands, reduces human effort, and yields higher performance than if humans directly controlled all agents. In conclusion, we show that humans can provide helpful advice to the RL agents, allowing them to improve learning in a multi-agent setting.

翻译：近期强化学习(RL)与人在环路(HitL)学习的进展，使人机协同更易于人类与AI智能体组建团队。将人类专业知识与经验融入智能系统，既能提升效率又能产生效益。然而，人机协同的成功程度及其相较于人类或AI单独运作的表现差异仍待明晰。本研究证明：从人类学习中汲取经验具有实效性，且在复杂仿真环境中，人机协同的表现优于人类操控型与全自主型AI智能体。我们针对关键基础设施防护开发了新型仿真器，聚焦AI无人机与人类团队协作防御机场免受敌袭的场景。通过设计交互界面，使人类能够有效辅助AI智能体。实验表明：相较于直接向人类或智能体学习，从策略修正中学习的智能体具有更快的收敛速度。此外，人机协同能降低认知负荷与时间需求，减少人工干预，并取得优于人类直接操控所有智能体的任务表现。结论表明，人类可为强化学习智能体提供有效建议，显著提升多智能体协同环境中的学习效率。