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Deep Reinforcement Learning in Complex Environments

Raia Hadsell ( Google )

Catastrophic forgetting is a long-acknowledged challenge for neural networks. This phenomenon has become increasingly relevant as we move beyond static datasets towards AGI systems that learn online, from sequential experience. I will describe new learning methods that enable stable, data-efficient end-to-end learning while mitigating forgetting, and show how these methods can be used to train agents to navigate complex simulated environments.

Speaker bio

Raia Hadsell, a senior research scientist at Google DeepMind, has worked on deep learning and robotics problems for over 10 years. Her thesis on Vision for Mobile Robots won the Best Dissertation award from New York University, and was followed by a post-doc at Carnegie Mellon’s Robotics Institute. Raia then worked as a senior scientist and tech manager at SRI International. Raia joined DeepMind in 2014, where she leads a research team studying robot navigation and lifelong learning.



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