4 papers · 1 filter
Reinforcement Learning from Rich Feedback with Distributional DAgger
Rishabh Agrawal, Jacob Fein-Ashley, Paria Rashidinejad
Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sample many responses and reward…
Balance Equation-based Distributionally Robust Offline Imitation Learning
Rishabh Agrawal, Yusuf Alvi, Rahul Jain +1
Imitation Learning (IL) has proven highly effective for robotic and control tasks where manually designing reward functions or explicit controllers is infeasible. However, standard…
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility
Rishabh Agrawal
Few-shot learning (FSL) enables machine learning models to generalize effectively with minimal labeled data, making it crucial for data-scarce domains such as healthcare, robotics,…
Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning
Rishabh Agrawal, Nathan Dahlin, Rahul Jain +1
Imitation learning (IL) is notably effective for robotic tasks where directly programming behaviors or defining optimal control costs is challenging. In this work, we address a sce…