1 citations · 2 across the 6 of their papers we have counts for
6 papers
Evaluating Feature Dependent Noise in Preference-based Reinforcement Learning
Yuxuan Li, Harshith Reddy Kethireddy, Srijita Das
Learning from Preferences in Reinforcement Learning (PbRL) has gained attention recently, as it serves as a natural fit for complicated tasks where the reward function is not easil…
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…
An LLM-Guided Tutoring System for Social Skills Training
Michael Guevarra, Indronil Bhattacharjee, Srijita Das +4
Social skills training targets behaviors necessary for success in social interactions. However, traditional classroom training for such skills is often insufficient to teach effect…
CANDERE-COACH: Reinforcement Learning from Noisy Feedback
Yuxuan Li, Srijita Das, Matthew E. Taylor
In recent times, Reinforcement learning (RL) has been widely applied to many challenging tasks. However, in order to perform well, it requires access to a good reward function whic…
GLIDE-RL: Grounded Language Instruction through DEmonstration in RL
Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha +2
One of the final frontiers in the development of complex human - AI collaborative systems is the ability of AI agents to comprehend the natural language and perform tasks according…
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…