1 citations · 1 across the 13 of their papers we have counts for
16 papers
Is Better Teacher Supervision Enough? Unlocking Student-side Learning in Multimodal On-Policy Distillation
Siyuan Liu, Kanghui Tian, Yue Duan +4
On-policy distillation (OPD) improves reasoning by providing token-level supervision from a teacher on a student's own trajectories. Existing methods primarily focus on enhancing t…
Learning Through Game: Skewed Transfer of Tabular Knowledge to Strengthen Image Model
Longfei Huang, Shangdong Yang, Yang Yang
Multimodal tabular-image learning is gaining growing attention, yet it faces challenges due to tabular data unavailable at test time. A practical solution involves transferring tab…
Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction
Xingguo Chen, Zhiang He, Yuchen Shen +4
Temporal-difference learning with function approximation can be unstable under off-policy sampling. TDC stabilizes off-policy TD through an auxiliary covariance correction, and TDR…
Behavior-Induced Mirror-Prox Temporal-Difference Learning for Faster Off-Policy Prediction
Xingguo Chen, Yuchen Shen, Shangdong Yang +3
Gradient temporal-difference methods provide stable off-policy prediction with linear function approximation, but their practical performance is strongly affected by the geometry i…
Regularized Centered Emphatic Temporal Difference Learning
Xingguo Chen, Chaohui Wu, Jinguo Ye +5
Off-policy temporal-difference (TD) learning with function approximation faces a structural tradeoff among stability, projection geometry, and variance control. Emphatic TD (ETD) i…
Bitboard version of Tetris AI
Xingguo Chen, Pingshou Xiong, Zhenyu Luo +6
The efficiency of game engines and policy optimization algorithms is crucial for training reinforcement learning (RL) agents in complex sequential decision-making tasks, such as Te…
OpenGuanDan: A Large-Scale Imperfect Information Game Benchmark
Chao Li, Shangdong Yang, Chiheng Zhan +5
The advancement of data-driven artificial intelligence (AI), particularly machine learning, heavily depends on large-scale benchmarks. Despite remarkable progress across domains ra…
AGC-Drive: A Large-Scale Dataset for Real-World Aerial-Ground Collaboration in Driving Scenarios
Yunhao Hou, Bochao Zou, Min Zhang +7
By sharing information across multiple agents, collaborative perception helps autonomous vehicles mitigate occlusions and improve overall perception accuracy. While most previous w…
DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing
Shengdong Han, Shangdong Yang, Xin Zhang +5
Resolving closely-spaced small targets in dense clusters presents a significant challenge in infrared imaging, as the overlapping signals hinder precise determination of their quan…
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation
Hongye Cao, Fan Feng, Jing Huo +4
Model-based offline Reinforcement Learning (RL) constructs environment models from offline datasets to perform conservative policy optimization. Existing approaches focus on learni…
Bellman Error Centering
Xingguo Chen, Yu Gong, Shangdong Yang +1
This paper revisits the recently proposed reward centering algorithms including simple reward centering (SRC) and value-based reward centering (VRC), and points out that SRC is ind…
A Variance Minimization Approach to Temporal-Difference Learning
Xingguo Chen, Yu Gong, Shangdong Yang +1
Fast-converging algorithms are a contemporary requirement in reinforcement learning. In the context of linear function approximation, the magnitude of the smallest eigenvalue of th…
Efficient Last-iterate Convergence Algorithms in Solving Games
Linjian Meng, Youzhi Zhang, Zhenxing Ge +6
To establish last-iterate convergence for Counterfactual Regret Minimization (CFR) algorithms in learning a Nash equilibrium (NE) of extensive-form games (EFGs), recent studies ref…
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient
Wubing Chen, Wenbin Li, Xiao Liu +2
Cooperative multi-agent policy gradient (MAPG) algorithms have recently attracted wide attention and are regarded as a general scheme for the multi-agent system. Credit assignment…
Keeping Minimal Experience to Achieve Efficient Interpretable Policy Distillation
Xiao Liu, Shuyang Liu, Wenbin Li +2
Although deep reinforcement learning has become a universal solution for complex control tasks, its real-world applicability is still limited because lacking security guarantees fo…
Online Attentive Kernel-Based Temporal Difference Learning
Guang Yang, Xingguo Chen, Shangdong Yang +3
With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficie…