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Recent Advances in Population-Based Search for Deep Neural Networks: Quality Diversity, Indirect Encodings, and Open-Ended Algorithms
Jeff Clune · Joel Lehman · Kenneth Stanley

Mon Jun 10 09:15 AM -- 11:30 AM (PDT) @ Hall A

We will cover new, exciting, unconventional techniques for improving population-based search. These ideas are already enabling us to solve hard problems. They also hold great promise for further advancing machine learning, including deep neural networks. Major topics covered include (1) explicitly searching for behavioral diversity (in a low-dimensional space where diversity is inherently interesting, such as the behavior of robots, rather than in the true search space, such as the weights of the DNN that controls the robot), especially Quality Diversity algorithms, which have produced state-of-the-art results in robotics and solved a version of the hard-exploration RL challenge of Montezuma’s Revenge; (2) open-ended search, wherein algorithms continually create new and increasingly complex capabilities without bound, for example by simultaneously inventing new challenges and their solutions; and (3) indirect encoding (e.g. HyperNEAT/HyperNetworks), wherein one network encodes how to construct a larger neural network or learning system. The idea is motivated by biological development, wherein a search in the space of a few thousand genes enables the specification of a trillion-connection brain and its learning algorithm. We conclude with a discussion on current and future hybrids of traditional machine learning with these ideas, including how augmenting meta-learning with them offers an alternative path to our most ambitious AI goals.

Author Information

Jeff Clune (Uber AI Labs)
Jeff Clune

Jeff Clune is the Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming and a Senior Research Manager and founding member of Uber AI Labs, which was formed after Uber acquired a startup he was a part of. Jeff focuses on robotics and training deep neural networks via deep learning, including deep reinforcement learning. Prior to becoming a professor, he was a Research Scientist at Cornell University and received degrees from Michigan State University (PhD, master’s) and the University of Michigan (bachelor’s). More on Jeff’s research can be found at JeffClune.com or on Twitter (@jeffclune).

Joel Lehman (Uber AI Labs)
Joel Lehman

Joel Lehman is a senior research scientist at Uber AI Labs, and previously was an assistant professor at the IT University of Copenhagen. His research spans AI safety, neuroevolution, reinforcement learning, and deep learning.

Kenneth Stanley (Uber AI and University of Central Florida)
Kenneth Stanley

Kenneth O. Stanley leads a research team at OpenAI on the challenge of open-endedness. He was previously Charles Millican Professor of Computer Science at the University of Central Florida and was also a co-founder of Geometric Intelligence Inc., which was acquired by Uber to create Uber AI Labs, where he was head of Core AI research. He received a B.S.E. from the University of Pennsylvania in 1997 and received a Ph.D. in 2004 from the University of Texas at Austin. He is an inventor of the Neuroevolution of Augmenting Topologies (NEAT), HyperNEAT, , novelty search, and POET algorithms, as well as the CPPN representation, among many others. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, interactive evolution, quality diversity, and open-endedness. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, novelty search, Galactic Arms Race, and POET. His original 2002 paper on NEAT also received the 2017 ISAL Award for Outstanding Paper of the Decade 2002 - 2012 from the International Society for Artificial Life. He is a coauthor of the popular science book, "Why Greatness Cannot Be Planned: The Myth of the Objective" (published by Springer), and has spoken widely on its subject.

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