activity
20192022
most citedSMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

103 citations · 144 across the 10 of their papers we have counts for

collaborators

14 papers

stat.ML20222 cited

Conformalized Fairness via Quantile Regression

Meichen Liu, Lei Ding, Dengdeng Yu +3

Algorithmic fairness has received increased attention in socially sensitive domains. While rich literature on mean fairness has been established, research on quantile fairness rema…

cs.LG20228 cited

LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction

Bowen Wang, Guibao Shen, Dong Li +7

Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation f…

cs.RO2021

Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles

Zhitao Wang, Yuzheng Zhuang, Qiang Gu +3

For autonomous vehicles integrating onto roadways with human traffic participants, it requires understanding and adapting to the participants' intention and driving styles by respo…

cs.AI202110 cited

Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment

Tianze Zhou, Fubiao Zhang, Kun Shao +10

Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination…

cs.AI20218 cited

Learning Symbolic Rules for Interpretable Deep Reinforcement Learning

Zhihao Ma, Yuzheng Zhuang, Paul Weng +4

Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks. However, this black-box approach fails to explain the learned policy i…

cs.LG20211 cited

Addressing Action Oscillations through Learning Policy Inertia

Chen Chen, Hongyao Tang, Jianye Hao +2

Deep reinforcement learning (DRL) algorithms have been demonstrated to be effective in a wide range of challenging decision making and control tasks. However, these methods typical…