most citedDropNAS: Grouped Operation Dropout for Differentiable Architecture Search

24 citations · 45 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2023

Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO

Yangkun Chen, Joseph Suarez, Junjie Zhang +18

We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-a…

cs.AI2022★ 14 cited

PerfectDou: Dominating DouDizhu with Perfect Information Distillation

Guan Yang, Minghuan Liu, Weijun Hong +4

As a challenging multi-player card game, DouDizhu has recently drawn much attention for analyzing competition and collaboration in imperfect-information games. In this paper, we pr…

cs.LG2022★ 6 cited

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas +19

Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more…

cs.LG2022★ 1 cited

Generative Adversarial Exploration for Reinforcement Learning

Weijun Hong, Menghui Zhu, Minghuan Liu +4

Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses o…

cs.LG2022★ 24 cited

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

Weijun Hong, Guilin Li, Weinan Zhang +4

Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation t…