3 papers
cs.LG2026
Learning from Imperfect Demonstrations via Temporal Behavior Tree-Guided Trajectory Repair
Aniruddh G. Puranic, Sebastian Schirmer, John S. Baras +1
Learning robot control policies from demonstrations is a powerful paradigm, yet real-world data is often suboptimal, noisy, or otherwise imperfect, posing significant challenges fo…
cs.LG2026
Polynomial Surrogate Training for Differentiable Ternary Logic Gate Networks
Sai Sandeep Damera, Ryan Matheu, Aniruddh G. Puranic +1
Differentiable logic gate networks (DLGNs) learn compact, interpretable Boolean circuits via gradient-based training, but all existing variants are restricted to the 16 two-input b…
cs.LG2025
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression
Clinton Enwerem, Aniruddh G. Puranic, John S. Baras +1
Mainstream approximate action-value iteration reinforcement learning (RL) algorithms suffer from overestimation bias, leading to suboptimal policies in high-variance stochastic env…