Human-AI Collaboration in Real-World Complex Environment with
  Reinforcement Learning
Md Saiful Islam Srijita Das Sai Krishna Gottipati William Duguay Clodéric Mars Jalal Arabneydi Antoine Fagette Matthew Guzdial Matthew-E-Taylor
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.