1 citations · 2 across the 5 of their papers we have counts for
5 papers
A Systematic Approach to Design Real-World Human-in-the-Loop Deep Reinforcement Learning: Salient Features, Challenges and Trade-offs
Jalal Arabneydi, Saiful Islam, Srijita Das +7
With the growing popularity of deep reinforcement learning (DRL), human-in-the-loop (HITL) approach has the potential to revolutionize the way we approach decision-making problems…
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning
Md Saiful Islam, Srijita Das, Sai Krishna Gottipati +6
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 exp…
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning
Rupali Bhati, Sai Krishna Gottipati, Clodéric Mars +1
While there has been significant progress in curriculum learning and continuous learning for training agents to generalize across a wide variety of environments in the context of s…
Human-Machine Teaming for UAVs: An Experimentation Platform
Laila El Moujtahid, Sai Krishna Gottipati, Clodéric Mars +1
Full automation is often not achievable or desirable in critical systems with high-stakes decisions. Instead, human-AI teams can achieve better results. To research, develop, evalu…
Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations
AI Redefined, Sai Krishna Gottipati, Sagar Kurandwad +3
Involving humans directly for the benefit of AI agents' training is getting traction thanks to several advances in reinforcement learning and human-in-the-loop learning. Humans can…