6 citations · 11 across the 14 of their papers we have counts for
15 papers
Hesitation-Aware On-Policy Distillation for Diffusion Language Models
Jianguo Huang, Lipeng Wan, Yanchen Deng +1
Diffusion large language models (dLLMs) generate text by iterative unmasking. At each denoising step, a dLLM proposes a token at every masked position, but the decoder commits only…
GeoBlock: Inferring Block Granularity from Dependency Geometry in Diffusion Language Models
Lipeng Wan, Junjie Ma, Jianhui Gu +3
Block diffusion enables efficient parallel refinement in diffusion language models, but its decoding behavior depends critically on block size. Existing block-sizing strategies rel…
Progressive Refinement Regulation for Accelerating Diffusion Language Model Decoding
Lipeng Wan, Jianhui Gu, Junjie Ma +4
Diffusion language models generate text through iterative denoising under a uniform refinement rule applied to all tokens. However, tokens stabilize at different rates in practice,…
Task-Aware Exploration via a Predictive Bisimulation Metric
Dayang Liang, Ruihan Liu, Lipeng Wan +2
Accelerating exploration in visual reinforcement learning under sparse rewards remains challenging due to the substantial task-irrelevant variations. Despite advances in intrinsic…
Playing Non-Embedded Card-Based Games with Reinforcement Learning
Tianyang Wu, Lipeng Wan, Yuhang Wang +2
Significant progress has been made in AI for games, including board games, MOBA, and RTS games. However, complex agents are typically developed in an embedded manner, directly acce…
MInCo: Mitigating Information Conflicts in Distracted Visual Model-based Reinforcement Learning
Shiguang Sun, Hanbo Zhang, Zeyang Liu +4
Existing visual model-based reinforcement learning (MBRL) algorithms with observation reconstruction often suffer from information conflicts, making it difficult to learn compact r…
Enhancing Decision Transformer with Diffusion-Based Trajectory Branch Generation
Zhihong Liu, Long Qian, Zeyang Liu +3
Decision Transformer (DT) can learn effective policy from offline datasets by converting the offline reinforcement learning (RL) into a supervised sequence modeling task, where the…
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model
Zeyang Liu, Xinrui Yang, Shiguang Sun +4
Recent progress in generative models has stimulated significant innovations in many fields, such as image generation and chatbots. Despite their success, these models often produce…
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
Zeyang Liu, Lipeng Wan, Xinrui Yang +3
Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intr…
MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenes
Han Wang, Jiayuan Zhang, Lipeng Wan +3
Manipulation relationship detection (MRD) aims to guide the robot to grasp objects in the right order, which is important to ensure the safety and reliability of grasping in object…
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning
Lipeng Wan, Zeyang Liu, Xingyu Chen +2
Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning methods with linear value decomposition (LVD) or monotonic value decompositio…
Multi-Agent Intention Sharing via Leader-Follower Forest
Zeyang Liu, Lipeng Wan, Xue sui +2
Intention sharing is crucial for efficient cooperation under partially observable environments in multi-agent reinforcement learning (MARL). However, message deceiving, i.e., a mis…
Greedy-based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning
Lipeng Wan, Zeyang Liu, Xingyu Chen +2
Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning methods with linear value decomposition (LVD) or monotonic value decompositio…
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation
Lipeng Wan, Xuwei Song, Xuguang Lan +1
Cooperative multi-agent tasks require agents to deduce their own contributions with shared global rewards, known as the challenge of credit assignment. General methods for policy b…
A Multi-task Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes
Hanbo Zhang, Xuguang Lan, Site Bai +3
Autonomous robotic grasping plays an important role in intelligent robotics. However, how to help the robot grasp specific objects in object stacking scenes is still an open proble…