2 citations · 4 across the 7 of their papers we have counts for
8 papers
When Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making
Yuanfan Li, Junyi Chen, Qi Zhou +3
Frontier language models excel at complex tasks yet may prioritize protection over larger physical consequences in simple, urgent decisions. GPT-5.6 Sol, for example, moves a girl…
When Denser Credit Is Not Enough: Evidence-Calibrated Policy Optimization for Long-Horizon LLM Agent Training
Yuanfan Li, Qi Zhou, Wenjing Duan +1
Long-horizon LLM agents require reinforcement learning methods that can assign credit to intermediate decisions under sparse and delayed rewards. Recent group-based methods such as…
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions
Qiyuan Liu, Qi Zhou, Rui Yang +1
Recent work has shown that representation learning plays a critical role in sample-efficient reinforcement learning (RL) from pixels. Unfortunately, in real-world scenarios, repres…
Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution
Jie Wang, Rui Yang, Zijie Geng +7
Generalization in partially observed markov decision processes (POMDPs) is critical for successful applications of visual reinforcement learning (VRL) in real scenarios. A widely u…
Efficient Exploration in Resource-Restricted Reinforcement Learning
Zhihai Wang, Taoxing Pan, Qi Zhou +1
In many real-world applications of reinforcement learning (RL), performing actions requires consuming certain types of resources that are non-replenishable in each episode. Typical…
Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization
Yufei Kuang, Miao Lu, Jie Wang +3
Deep reinforcement learning algorithms can perform poorly in real-world tasks due to the discrepancy between source and target environments. This discrepancy is commonly viewed as…
Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic
Zhihai Wang, Jie Wang, Qi Zhou +2
Model-based reinforcement learning algorithms, which aim to learn a model of the environment to make decisions, are more sample efficient than their model-free counterparts. The sa…
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
Qi Zhou, Houqiang Li, Jie Wang
Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-free methods. However, due to the inevitable errors of learned models, model-based…