Deep Reinforcement Learning for Social Learning Fun Chat, Natasha Jacques, Google

Natasha Jaques is currently a Research Scientist at Google Brain and postdoc fellow at UC Berkeley, where her research interests are in designing multiagent RL algorithms while focusing on social reinforcement learning, that can improve generalization, coordination between agents, and collaboration between human and AI agents. She received her PhD from Massachusetts Institute of Technology (MIT) where she focused on Affective Computing and other techniques for deep, reinforcement learning. She has also received multiple awards for her research works submitted to venues like ICML and NeurIPS She has interned atDeepMind, Google Brain, and is an OpenAI Scholars mentor. 00:00 Introductions 01:25 Can you tell us a bit about what projects you are working on at Google currently And what does the work routine look like as a Research Scientist 06:25 You have worked as a researcher at many diverse backgrounds who are leading in the domain of machine learning: MIT, Google Brain, DeepMind what are the key diff