Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning

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 exten...

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Bibliographic Details
Published in:arXiv.org 2023-12
Main Authors: Md Saiful Islam, Das, Srijita, Gottipati, Sai Krishna, Duguay, William, Mars, Clodéric, Arabneydi, Jalal, Fagette, Antoine, Guzdial, Matthew, Matthew-E-Taylor
Format: Article
Language:English
Subjects:
Collaboration
Cooperation
Critical infrastructure
Multiagent systems
Reagents
Teams
Online Access:Get full text
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Summary: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.
ISSN:2331-8422