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Tuesday, June 30 • 3:15pm - 4:05pm
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions - Rui Wang, Uber

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Reinforcement learning (RL) is good at solving some hand-designed challenges. However, in real-life applications such as robotics, there are many edge cases that are impossible to design by hand, and some of them are too challenging to solve from scratch.

We introduce a novel class of RL algorithms (named POET) which combines the concept of open-endedness with RL. Such algorithms create increasingly diverse and challenging problems all by itself, while automatically forming a curriculum that teaches RL agents to solve those problems. This talk covers our most recent significant enhancements to POET, which is NEVER presented before: we enable POET to be applied to any general domains, introduces quantitative measurement of open-ended progress, and formulate a much more expressive environmental encoding. The combination of all of these innovation leads to the most open-ended demonstration so far: in a single run of the algorithm, the capability of agents increase along with the difficult levels/diversity of the created tasks, finally they solving extremely challenging tasks that cannot be solved by direct optimization or by learning through a hand-designed curriculum.

avatar for Rui Wang

Rui Wang

Senior Research Scientist, Uber
Dr. Rui Wang holds a Ph.D. in Electrical and Computer Engineering from University of Illinois at Urbana Champaign with 10 years of professional experiences in software and internet industry. Rui is currently a Senior Research Scientist at Uber AI, leading research and engineering... Read More →

Tuesday June 30, 2020 3:15pm - 4:05pm CDT
AI/ML/DL Theater