79 citations · 165 across the 24 of their papers we have counts for
26 papers
Can We Trust the Teacher? Decoupled Credit Direction-Magnitude for Self-Distillation
Yugu Li, Zehong Cao, Peizhen Li +3
RLVR provides reliable trajectory-level credit, while OPSD offers dense supervision for token-level credit. This exposes a fundamental coupling when updating step-level credit dire…
RTPO: Reverse-Turn Policy Optimization for Stabilizing Agentic RL Training
Yugu Li, Zehong Cao, Jianglin Qiao +1
Training multi-turn agentic workflows with reinforcement learning (RL) enables large language models to perform complex reasoning, use external tools, and conduct iterative search…
3D Scene Graph Prediction: Generating Hierarchical Models from Partially Observed Environments
Siyi Hu, Jared Strader, Hyungtae Lim +1
Generating realistic 3D indoor scenes is an area of growing interest in computer vision and robotics. Existing methods, often motivated by applications such as interior design, gen…
HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster
Mohamad A. Hady, Muhammad Anwar Masum, Siyi Hu +3
This work addresses the problem of autonomous resource management in heterogeneous satellite cluster conducting Earth Observation (EO) missions including optical and Synthetic Aper…
KD-MARL: Resource-Aware Knowledge Distillation in Multi-Agent Reinforcement Learning
Monirul Islam Pavel, Siyi Hu, Muhammad Anwar Masum +3
Real world deployment of multi agent reinforcement learning MARL systems is fundamentally constrained by limited compute memory and inference time. While expert policies achieve hi…
Multi-Agent Reinforcement Learning for Heterogeneous Satellite Cluster Resources Optimization
Mohamad A. Hady, Siyi Hu, Mahardhika Pratama +2
This work investigates resource optimization in heterogeneous satellite clusters performing autonomous Earth Observation (EO) missions using Reinforcement Learning (RL). In the pro…
Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review
Siyi Hu, Mohamad A Hady, Jianglin Qiao +3
Multi-Agent Reinforcement Learning (MARL) has achieved strong performance in simulated benchmarks, yet real deployments often violate the assumptions under which algorithms are des…
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study
Mohamad A. Hady, Siyi Hu, Mahardhika Pratama +2
The exponential growth of Low Earth Orbit (LEO) satellites has revolutionised Earth Observation (EO) missions, addressing challenges in climate monitoring, disaster management, and…
Onboard Optimization and Learning: A Survey
Monirul Islam Pavel, Siyi Hu, Mahardhika Pratama +1
Onboard learning is a transformative approach in edge AI, enabling real-time data processing, decision-making, and adaptive model training directly on resource-constrained devices…
Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey
Mohamad A. Hady, Siyi Hu, Mahardhika Pratama +2
Multi-Agent Reinforcement Learning (MARL) has become a powerful framework for numerous real-world applications, modeling distributed decision-making and learning from interactions…
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning
Yugu Li, Zehong Cao, Jianglin Qiao +1
In cooperative multi-agent reinforcement learning (MARL), agents typically form a single grand coalition based on credit assignment to tackle a composite task, often resulting in s…
Mixed Diffusion for 3D Indoor Scene Synthesis
Siyi Hu, Diego Martin Arroyo, Stephanie Debats +3
Generating realistic 3D scenes is an area of growing interest in computer vision and robotics. However, creating high-quality, diverse synthetic 3D content often requires expert in…
Debunking Disinformation: Revolutionizing Truth with NLP in Fake News Detection
Li He, Siyi Hu, Ailun Pei
The Internet and social media have altered how individuals access news in the age of instantaneous information distribution. While this development has increased access to informat…
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Ceyao Zhang, Kaijie Yang, Siyi Hu +12
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely…
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
Jiarong Liu, Yifan Zhong, Siyi Hu +4
Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to samp…
Foundations of Spatial Perception for Robotics: Hierarchical Representations and Real-time Systems
Nathan Hughes, Yun Chang, Siyi Hu +4
3D spatial perception is the problem of building and maintaining an actionable and persistent representation of the environment in real-time using sensor data and prior knowledge.…
Heterogeneous-Agent Reinforcement Learning
Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng +3
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research. However, many research endeavours hea…
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
Siyi Hu, Yifan Zhong, Minquan Gao +6
A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible d…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Semantic 3D scene understanding is a problem of critical importance in robotics. While significant advances have been made in simultaneous localization and mapping algorithms, robo…
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL
Siyi Hu, Chuanlong Xie, Xiaodan Liang +1
Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving tasks in a grid world and real-world scenarios, in which agents are given different attri…
Neural Trees for Learning on Graphs
Rajat Talak, Siyi Hu, Lisa Peng +1
Graph Neural Networks (GNNs) have emerged as a flexible and powerful approach for learning over graphs. Despite this success, existing GNNs are constrained by their local message-p…
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Siyi Hu, Fengda Zhu, Xiaojun Chang +1
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model…
Multi-view Drone-based Geo-localization via Style and Spatial Alignment
Siyi Hu, Xiaojun Chang
In this paper, we focus on the task of multi-view multi-source geo-localization, which serves as an important auxiliary method of GPS positioning by matching drone-view image and s…