PAIR Lab: PKU Alignment and Interaction Research Lab
PAIR Lab: PKU Alignment and Interaction Research Lab
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1
TorchOpt: An Efficient Library for Differentiable Optimization
Recent years have witnessed the booming of various differentiable optimization algorithms. These algorithms exhibit different execution …
Jie Ren
,
Xidong Feng
,
Bo Liu
,
Xuehai Pan
,
Yao Fu
,
Luo MAI
,
Yaodong Yang
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Scalable Model-based Policy Optimization for Decentralized Networked Systems
Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple …
Yali Du
,
Chengdong Ma
,
Yuchen Liu
,
Runji LIN
,
Hao Dong
,
Jun Wang
,
Yaodong Yang
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GenDexGrasp: Generalizable Dexterous Grasping
Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods …
Puhao Li
,
Tengyu Liu
,
Yuyang Li
,
Yiran Geng
,
Yixin Zhu
,
Yaodong Yang
,
Siyuan Huang
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A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
N/A
Bo Liu
,
Xidong Feng
,
Jie Ren
,
Luo MAI
,
Rui Zhu
,
Haifeng ZHANG
,
Jun Wang
,
Yaodong Yang
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A Unified Diversity Measure for Multiagent Reinforcement Learning
Promoting behavioural diversity is of critical importance in multi-agent reinforcement learning, since it helps the agent population …
Zongkai Liu
,
Chao Yu
,
Yaodong Yang
,
peng sun
,
Zifan Wu
,
Yuan Li
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Constrained Update Projection Approach to Safe Policy Optimization
Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring …
Long Yang
,
Jiaming Ji
,
Juntao Dai
,
Linrui Zhang
,
Binbin Zhou
,
Pengfei Li
,
Yaodong Yang
,
Gang Pan
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MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control
We introduce the Multi-Agent Tracking Environment (MATE), a novel multi-agent environment simulates the target coverage control …
Xuehai Pan
,
Mickel Liu
,
Fangwei Zhong
,
Yaodong Yang
,
Song-Chun Zhu
,
Yizhou Wang
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Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning
Setting up a well-designed reward function has been challenging for many reinforcement learning applications. Preference-based …
Runze Liu
,
Fengshuo Bai
,
Yali Du
,
Yaodong Yang
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Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation even at the …
Yuanpei Chen
,
Tianhao Wu
,
Shengjie Wang
,
Xidong Feng
,
Jiechuang Jiang
,
Stephen Marcus McAleer
,
Hao Dong
,
Zongqing Lu
,
Song-Chun Zhu
,
Yaodong Yang
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End-to-End Affordance Learning for Robotic Manipulation
Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In …
Yiran Geng
,
Boshi An
,
Haoran Geng
,
Yuanpei Chen
,
Yaodong Yang
,
Hao Dong
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