7 papers
Real-Time Generative Policy via Langevin-Guided Flow Matching for Autonomous Driving
Tianze Zhu, Yinuo Wang, Wenjun Zou +6
Reinforcement learning (RL) is a fundamental methodology in autonomous driving systems, where generative policies exhibit considerable potential by leveraging their ability to mode…
World In Your Hands: A Large-Scale and Open-Source Ecosystem for Learning Human-Centric Manipulation in the Wild
Yupeng Zheng, Jichao Peng, Weize Li +22
We introduce World In Your Hands (WIYH), a large-scale open-source ecosystem comprising over 1,000 hours of human manipulation data collected in-the-wild with millimeter-scale moti…
Bootstrap Off-policy with World Model
Guojian Zhan, Likun Wang, Xiangteng Zhang +3
Online planning has proven effective in reinforcement learning (RL) for improving sample efficiency and final performance. However, using planning for environment interaction inevi…
Off-policy Reinforcement Learning with Model-based Exploration Augmentation
Likun Wang, Xiangteng Zhang, Yinuo Wang +5
Exploration is fundamental to reinforcement learning (RL), as it determines how effectively an agent discovers and exploits the underlying structure of its environment to achieve o…
Mind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL
Guojian Zhan, Likun Wang, Pengcheng Wang +4
Maximum entropy has become a mainstream off-policy reinforcement learning (RL) framework for balancing exploitation and exploration. However, two bottlenecks still limit further pe…
Distributional Soft Actor-Critic with Diffusion Policy
Tong Liu, Yinuo Wang, Xujie Song +6
Reinforcement learning has been proven to be highly effective in handling complex control tasks. Traditional methods typically use unimodal distributions, such as Gaussian distribu…