6 papers
Newton Method for Fixed-Support Doubly Entropic Wasserstein Barycenter
Jianting Pan, Sirong Dai, Lei Yang +3
We study the fixed-support doubly regularized Wasserstein barycenter problem. Using the semi-dual formulation of entropic optimal transport, we reformulate the problem as a smooth,…
Population-Free Pareto Tracking for Sample-Efficient Multi-Policy MORL
Zeyu Zhao, Yueling Che, Kaichen Liu +2
Multi-objective reinforcement learning (MORL) is a fundamental framework for real-world decision-making problems involving multiple conflicting criteria. Existing multi-policy (MP)…
OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation
Yunyang Mo, Jian Li, Qiwei Wu +2
While reinforcement learning (RL) enables robots to acquire skills autonomously, its real-world deployment is severely limited by inefficient and unsafe exploration. Human-in-the-l…
Inexact Bregman Sparse Newton Method for Efficient Optimal Transport
Jianting Pan, Ji'an Li, Ming Yan
Computing exact Optimal Transport (OT) distances for large-scale datasets is computationally prohibitive. While entropy-regularized alternatives offer speed, they sacrifice precisi…
Improving Search Agent with One Line of Code
Jian Li, Dongsheng Chen, Zhenhua Xu +5
Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools for a multi-turn information-seeking…
Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
Bo Yue, Jian Li, Guiliang Liu
Optimizing objective functions subject to constraints is fundamental in many real-world applications. However, these constraints are often not readily defined and must be inferred…