103 citations · 200 across the 68 of their papers we have counts for
7 papers · 1 filter
SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations
Wei-Jer Chang, Zhouchonghao Wu, Akshay Rangesh +3
Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints, and behaviors differ fundament…
SPACeR: Self-Play Anchoring with Centralized Reference Models
Wei-Jer Chang, Akshay Rangesh, Kevin Joseph +4
Developing autonomous vehicles (AVs) requires not only safety and efficiency, but also realistic, human-like behaviors that are socially aware and predictable. Achieving this requi…
LANGTRAJ: Diffusion Model and Dataset for Language-Conditioned Trajectory Simulation
Wei-Jer Chang, Wei Zhan, Masayoshi Tomizuka +2
Evaluating autonomous vehicles with controllability enables scalable testing in counterfactual or structured settings, enhancing both efficiency and safety. We introduce LangTraj,…
Residual Policy Gradient: A Reward View of KL-regularized Objective
Pengcheng Wang, Xinghao Zhu, Yuxin Chen +3
Reinforcement Learning and Imitation Learning have achieved widespread success in many domains but remain constrained during real-world deployment. One of the main issues is the ad…
Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives
Qinsi Wang, Jinghan Ke, Masayoshi Tomizuka +3
We provide a new LLM-compression solution via SVD, unlocking new possibilities for LLM compression beyond quantization and pruning. We point out that the optimal use of SVD lies in…
Strategically Conservative Q-Learning
Yutaka Shimizu, Joey Hong, Sergey Levine +1
Offline reinforcement learning (RL) is a compelling paradigm to extend RL's practical utility by leveraging pre-collected, static datasets, thereby avoiding the limitations associa…
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay
Catherine Weaver, Chen Tang, Ce Hao +3
Imitation learning learns a policy from demonstrations without requiring hand-designed reward functions. In many robotic tasks, such as autonomous racing, imitated policies must mo…