Aleksandra Faust

Toward Scalable Autonomy

Reinforcement learning is a promising technique for training autonomous systems that perform complex tasks in the real world. However, training reinforcement learning agents is difficult and tedious, requiring heavy engineering and often resulting in suboptimal results. In fact, we can formulate the interaction between the human engineer and the agent under training as a decision-making process that the human agent performs, and consequently automate the training by learning a decision making policy. In this talk we will cover several examples that illustrate the process, learning intrinsic rewards, RL loss functions, and curriculum for continual learning. We show that across different applications, learning to learn methods improve reinforcement learning agents generalization and performance, and raise questions about nurture vs nature in training autonomous systems.

Aleksandra Faust is a Senior Staff Research Scientist and Reinforcement Learning research team co-founder at Google Brain Research. Previously, Aleksandra founded and led Task and Motion Planning research in Robotics at Google, machine learning for self-driving car planning and controls in Waymo, and was a senior researcher in Sandia National Laboratories. She earned a Ph.D. in Computer Science at the University of New Mexico (with distinction), and a Master's in Computer Science from the University of Illinois at Urbana-Champaign. Her research interests include learning for safe and scalable reinforcement learning, learning to learn, motion planning, decision-making, and robot behavior. Aleksandra won IEEE RAS Early Career Award for Industry, the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in the period of 2011-2014, and was named Distinguished Alumna by the University of New Mexico School of Engineering. Her work has been featured in the New York Times, PC Magazine, ZdNet, VentureBeat, and was awarded Best Paper in Service Robotics at ICRA 2018, Best Paper in Reinforcement Learning for Real Life (RL4RL) at ICML 2019, and Best Paper of IEEE Computer Architecture Letters in 2020.

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