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Home Authors Posts by Yulun Li

Yulun Li

2 BLOG ARTICLES 2 RESEARCH PAPERS
Yulun Li previously worked as a software engineer with Uber AI.

Engineering Blog Articles

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Yulun Li
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Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Jeff Clune and Kenneth Stanley were co-senior authors on this work and our associated research paper.

Machine learning (ML) powers many technologies and services that underpin Uber’s platforms, and we invest in advancing fundamental ML research and engaging with …

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

Yulun Li
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0
Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning

This research was conducted with valuable help from collaborators at Google Brain and OpenAI.

A selection of trained agents populating the Atari zoo.

Some of the most exciting advances in AI recently have come from the field of deep reinforcement …

Research Papers

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

R. Wang, J. Lehman, A. Rawal, J. Zhi, Y. Li, J. Clune, K. Stanley
Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate progress in machine learning. A recent step in this direction is the Paired Open-Ended Trailblazer (POET), an algorithm that generates and solves its own challenges, and allows solutions to goal-switch between challenges to avoid local optima. Here we introduce and empirically validate two new innovations to the original algorithm, as well as two external innovations designed to help elucidate its full potential. [...] [PDF]
International Conference on Machine Learning (ICML), 2020

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]
2018

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