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Rui Wang

Rui Wang
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Rui Wang is a research scientist with Uber AI Labs.

Engineering Blog Articles

Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning

Uber AI Labs releases Atari Model Zoo, an open source repository of both trained Atari Learning Environment agents and tools to better understand them.

POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the...

Uber AI Labs introduces the Paired Open-Ended Trailblazer (POET), an algorithm that leverages open-endedness to push the bounds of machine learning.

VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution

Uber AI Labs introduces Visual Inspector for Neuroevolution (VINE), an open source interactive data visualization tool to help neuroevolution researchers better understand this family of algorithms.

Research Papers

Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their...

R. Wang, J. Lehman, J. Clune, K. Stanley
While the history of machine learning so far encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is whether the problems themselves can be generated by the algorithm at the same time as they are being solved. [...] [PDF at arXiv]
arXiv, 2019

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

F. Such, V. Madhavan, R. Liu, R. Wang, P. Castro, Y. Li, L. Schubert, M. Bellemare, J. Clune, J. Lehman
Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of reinforcement learning (RL) algorithms. [...] [PDF at arXiv]
arXiv, 2019

VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution

R. Wang, J. Clune, K. Stanley
Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems. However, it remains a challenge to analyze and interpret the underlying process of neuroevolution in such high dimensions. To begin to address this challenge, this paper presents an interactive data visualization tool called VINE (Visual Inspector for NeuroEvolution) aimed at helping neuroevolution researchers and end-users better understand and explore this family of algorithms. [...] [PDF on arXiv]
Visualization Workshop at The Genetic and Evolutionary Computation Conference (GECCO), 2018

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