Recent Advances in Population-Based Search for Deep Neural Networks: Quality Diversity, Indirect Encodings, and Open-Ended Algorithms
Recent Advances in Population-Based Search for Deep Neural Networks: Quality Diversity, Indirect Encodings, and Open-Ended Algorithms


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.


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. Since 2015, he won the Presidential Early Career Award for Scientists and Engineers from the White House, had papers on the cover of Nature and PNAS, won an NSF CAREER award, received an Outstanding Paper of the Decade award, and had best paper awards, oral presentations, and invited talks at the top machine learning conferences (NeurIPS, CVPR, ICLR, and ICML). His research is regularly covered in the press, including the New York Times, NPR, NBC, Wired, the BBC, the Economist, Science, Nature, National Geographic, the Atlantic, and the New Scientist. 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

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