Rose E. Wang

I am currently a first year PhD student at Stanford University. I am grateful to be funded by the NSF Graduate Research Fellowship.

During my undergraduate studies at MIT, I worked with Professor Josh Tenenbaum, Professor Jonathan How, Google Brain (student researcher) and Google Brain Robotics (internship). In a prior lifetime, I was a passionate multilinguist (Chinese, HSK Level 6; French, DELF B2; Spanish, DELE B2) and graduated with honors from Germany (Abitur with European plurilingual excellence award).

[ Email  /  Github  /  Twitter  /  Google Scholar  /  Blog ]

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News

Research

hpp Too many cooks: Bayesian inference for coordinating multi-agent collaboration
Rose E. Wang*, Sarah Wu*, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, Max Kleiman-Weiner
Journal of the Cognitive Science Society, April 2021.
NeurIPS 2020 Cooperative AI workshop.
[ Paper / Video / Code ]

Won best paper award at NeurIPS 2020 Cooperative AI Workshop!

We develop Bayesian Delegation, a decentralized multi-agent learning mechanism that enables agents to rapidly infer the sub-tasks of others by inverse planning. We demonstrate that our model is a capable ad-hoc collaborator, scales with team size and makes inferences about intent similar to human observers.

hpp Model-based Reinforcement Learning for Multiagent Goal Alignment
Rose E. Wang, J.Chase Kew, Dennis Lee, Tsang-Wei Edward Lee, Tingnan Zhang, Brian Ichter, Jie Tan, Aleksandra Faust
Conference on Robot Learning (CoRL) 2020.
Mentioned in Google AI Year in Review, 2020.

[ Paper / Video / Project Page / Blog post ]

In this work, we present hierarchical predictive planning (HPP) for decentralized multiagent navigation tasks. Our approach is trained in simulation and works in unseen settings both in simulation and in the real world (zero shot transfer)!

hpp Too many cooks: Coordinating multi-agent collaboration through inverse planning
Rose E. Wang*, Sarah Wu*, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, Max Kleiman-Weiner
Human-Like Machine Intelligence (book published with Oxford University Press)
Annual Meeting of the Cognitive Science Society (CogSci) 2020
International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 2020
Invited paper to OptLearnMAS Workshop at AAMAS 2020
[ Paper / Video / Code ]

Won best paper award for Computational Modeling for Higher Cognition at CogSci 2020!

We develop Bayesian Delegation, a decentralized multi-agent learning mechanism that enables agents to rapidly infer the sub-tasks of others by inverse planning.

rmaddpg R-MADDPG for Partially Observable Environments and Limited Communication
Rose E. Wang, Michael Everett, Jonathan P. How
International Conference on Machine Learning (ICML) 2019, Reinforcement Learning for Real Life Workshop
[ Paper / Code / Project Page ]

This paper introduces a deep recurrent multiagent actor-critic framework (R-MADDPG) for handling multiagent coordination under partial observable settings and limited communication.

rc66 DRIV3N: Race to Autonomy
Rose E. Wang, Austin Floyd, Marwa Abdulhai, Luxas Novak, David Klee, Sean Patrick Kelley
Robotics: Science and Systems I, 2017.
[ Video / Project Page ]

A whirlwind of an experience where my team and I developed a fast, autonomous, ~maze-solving~ racecars equipped with no machine learning technology and a decorative safety controller.

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