Abstract: Quality Diversity (QD) algorithms are those that seek to produce a diverse set of high-performing solutions to problems. I will describe them and a number of their positive attributes. I will summarize how they enable robots, after being damaged, to adapt in 1-2 minutes in order to continue performing their mission. I will next describe our QD-based Go-Explore algorithm, which dramatically improves the ability of deep reinforcement learning algorithms to solve previously unsolvable problems wherein reward signals are sparse, meaning that intelligent exploration is required. Go-Explore solved all unsolved Atari games, including Montezuma’s Revenge and Pitfall, considered by many to be a grand challenges of AI research. I will next motivate research into open-ended algorithms, which seek to innovate endlessly, and introduce our POET algorithm, which generates its own training challenges while learning to solve them, automatically creating a curricula for robots to learn an expanding set of diverse skills. Finally, I’ll argue that an alternate paradigm—AI-generating algorithms (AI-GAs)—may be the fastest path to accomplishing our field’s grandest ambition of creating general AI, and describe how QD, Open-Ended, and unsupervised pre-training algorithms (e.g. our recent work on video pre-training/VPT) will likely be essential ingredients of AI-GAs.
Bio: Jeff Clune is an Associate Professor of Computer Science at the University of British Columbia and a Faculty Member at the Vector Institute.
Previously, he was a Research Team Leader at OpenAI. Before that he was a Senior Research Manager and founding member of Uber AI Labs, which was formed after Uber acquired a startup our startup. Prior to Uber, he was the Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming.
He conducts research in three related areas of machine learning (and combinations thereof):
- Deep Learning: Improving our understanding of deep neural networks, harnessing them in novel applications, and advancing deep reinforcement learning
- Evolving Neural Networks: Investigating open questions in evolutionary biology regarding how intelligence evolved and harnessing those discoveries to improve our ability to evolve more complex, intelligent neural networks
- Robotics: Making robots more like animals in being adaptable and resilient
A good way to learn about Jeff's research is by visiting Google Scholar page, which lists all of his publications.
Jeff Clune, PhD
Associate Professor, Computer Science at University of British Columbia | Canada CIFAR AI Chair at Vector Institute | Senior Research Advisor at DeepMind