no code implementations • 12 Mar 2024 • Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John Dolan, Jeff Schneider
Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to parameterize autoregressive closed-loop models without retraining.
no code implementations • 9 Feb 2024 • Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke, Ayush Jain, Jeff Schneider, Katerina Fragkiadaki
Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function.
no code implementations • CVPR 2023 • Qiang He, Huangyuan Su, Jieyu Zhang, Xinwen Hou
In this work, we demonstrate that the learned representation of the $Q$-network and its target $Q$-network should, in theory, satisfy a favorable distinguishable representation property.
no code implementations • 22 Sep 2021 • Qiang He, Huangyuan Su, Chen Gong, Xinwen Hou
During the training of a reinforcement learning (RL) agent, the distribution of training data is non-stationary as the agent's behavior changes over time.