7 papers
Do VLAs Know They Will Fail? Interpretable Success and Failure Patterns in Physical AI Systems
Christian A. Rogers, Qian Lin, Daniel S. Brown
Vision-Language-Action (VLA) models have emerged as powerful general-purpose robot policies, but their successes and failures remain difficult to diagnose and understand. In this w…
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
Ali Larian, Qian Lin, Chang Zong Wu +1
As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such ch…
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding
Shenghong He, Chao Yu
Real-time bidding (RTB) plays a pivotal role in online advertising ecosystems. Advertisers employ strategic bidding to optimize their advertising impact while adhering to various f…
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization
Zongkai Liu, Qian Lin, Chao Yu +4
Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. Compared to single-agent case, m…
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning
Qian Lin, Zongkai Liu, Danying Mo +1
In recent years, significant progress has been made in multi-objective reinforcement learning (RL) research, which aims to balance multiple objectives by incorporating preferences…
Policy-regularized Offline Multi-objective Reinforcement Learning
Qian Lin, Chao Yu, Zongkai Liu +1
In this paper, we aim to utilize only offline trajectory data to train a policy for multi-objective RL. We extend the offline policy-regularized method, a widely-adopted approach f…
Off-Policy Primal-Dual Safe Reinforcement Learning
Zifan Wu, Bo Tang, Qian Lin +5
Primal-dual safe RL methods commonly perform iterations between the primal update of the policy and the dual update of the Lagrange Multiplier. Such a training paradigm is highly s…