My Google scholar page is here with the full list of papers including pre-prints.
Publications
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Learning to Design and Construct Bridge without Blueprint (IROS 2021)
Yunfei Li, Tao Kong, Lei Li, Yifeng Li and Yi Wu |
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Temporal Induced Self-Play for Stochastic Bayesian Games (IJCAI 2021)
Weizhe Chen*, Zihan Zhou*, Yi Wu, Fei Fang |
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Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization (ICLR 2021)
Zhenggang Tang*, Chao Yu*, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Shaolei Du, Yu Wang, Yi Wu |
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Solving Compositional Reinforcement Learning Problems via Task Reduction (ICLR 2021)
Yunfei Li, Yilin Wu, Huazhe Xu, Xiaolong Wang, Yi Wu |
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Multi-Task Reinforcement Learning with Soft Modularization (NeurIPS 2020)
Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang |
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Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (Findings of EMNLP 2020)
Shusheng Xu, Xingxing Zhang, Yi Wu, Furu Wei, Ming Zhou Source code can be found here |
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Emergent Tool Use from Multi-Agent Autocurricula (ICLR 2020, Spotlight)
Bowen Baker*, Ingmar Kanitscheider*, Todor Markov*, Yi Wu*, Glenn Powell, Bob McGrew, Igor Mordatch* (*team project) Check out our blog post with the most popular video ever in OpenAI's history :) |
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Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning (ICLR 2020)
Qian Long*, Zihan Zhou*, Abhibav Gupta, Fei Fang, Yi Wu†, Xiaolong Wang† († equal advising) Project website and source code can be found here |
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Influence-Based Multi-Agent Exploration (ICLR 2020, Spotlight)
Tonghan Wang*, Jianhao Wang*, Yi Wu, Chongjie Zhang The full version with appendix can be found here |
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Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient (AAAI 2019, Oral)
Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, Stuart Russell Source code can be found here |
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Deep Reinforcement Learning for Green Security Games with Real-Time Information (AAAI 2019, Oral)
Yufei Wang, Zheyuan Ryan Shi, Lantao Yu, Yi Wu, Rohit Singh, Lucas Joppa, Fei Fang |
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Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms (ICML 2018)
Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon S. Du, Stuart Russell A short version was accepted at PPS workshop, POPL 2017 |
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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments (NIPS 2017)
Ryan Lowe*, Yi Wu*, Aviv Tamar, Jean Harb, Pieter Abbeel, Igor Mordatch (* equal contribution) The OpenAI blog for this work is here |
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Adversarial Training for Relation Extraction (EMNLP 2017)
Yi Wu, David Bamman, Stuart Russell Source code can be found here |
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A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models (AAAI 2017, Oral)
Yusuf B. Erol*, Yi Wu*, Lei Li, Stuart Russell (* equal contribution) Supplementary materials are here |
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Value Iteration Network (NIPS 2016, Best Paper Award)
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel |
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Understanding and Evaluating Sparse Linear Discriminant Analysis (AISTATS 2015, Oral)
Yi Wu, David Wipf, Jeong-Min Yun |
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Dual Space Analysis of the Sparse Linear Model (NIPS2012)
David Wipf, Yi Wu |
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Selected Workshop Papers
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Building Generalizable Agents with a Realistic and Rich 3D Environment
(NIPS 2017 Deep Reinforcement Learning Symposium; ICLR 2018 Workshop Track) Check the Github page of our House3D project |
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BFiT: From Possible-World Semantics to Random-Evaluation Semantics in an Open Universe
(NIPS 2014 workshop on Probabilistic Programming, spotlight) |
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Thesis
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On Building Generalizable Learning Agents
Fall 2019 at UC Berkeley |
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