7 citations · 16 across the 19 of their papers we have counts for
9 papers · 1 filter
ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
Dongxiu Liu, Haoyi Niu, Peng Cheng +5
In the physical world we inhabit, space and time are fundamentally continuous. However, existing machine learning paradigms for world modeling are largely confined to discrete-time…
Skill Expansion and Composition in Parameter Space
Tenglong Liu, Jianxiong Li, Yinan Zheng +4
Humans excel at reusing prior knowledge to address new challenges and developing skills while solving problems. This paradigm becomes increasingly popular in the development of aut…
Are Expressive Models Truly Necessary for Offline RL?
Guan Wang, Haoyi Niu, Jianxiong Li +3
Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL…
Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Driving
Haoyi Niu, Qimao Chen, Yingyue Li +2
The deployment of autonomous vehicles (AVs) has faced hurdles due to the dominance of rare but critical corner cases within the long-tail distribution of driving scenarios, which n…
Continual Driving Policy Optimization with Closed-Loop Individualized Curricula
Haoyi Niu, Yizhou Xu, Xingjian Jiang +1
The safety of autonomous vehicles (AV) has been a long-standing top concern, stemming from the absence of rare and safety-critical scenarios in the long-tail naturalistic driving d…
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Haoyi Niu, Tianying Ji, Bingqi Liu +7
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging. Online R…
(Re)H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning
Haoyi Niu, Kun Ren, Yizhou Xu +4
Autonomous driving and its widespread adoption have long held tremendous promise. Nevertheless, without a trustworthy and thorough testing procedure, not only does the industry str…
Discriminator-Guided Model-Based Offline Imitation Learning
Wenjia Zhang, Haoran Xu, Haoyi Niu +5
Offline imitation learning (IL) is a powerful method to solve decision-making problems from expert demonstrations without reward labels. Existing offline IL methods suffer from sev…
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
Haoyi Niu, Shubham Sharma, Yiwen Qiu +4
Learning effective reinforcement learning (RL) policies to solve real-world complex tasks can be quite challenging without a high-fidelity simulation environment. In most cases, we…