RLHF: How to Learn from Human Feedback with Reinforcement Learning

This lecture was delivered at the 2023 Cooperative AI Summer School. For more information, please visit www.cooperativeai.com/summer-....
Natasha Jaques is a Senior Research Scientist at Google Brain. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. Natasha completed her PhD at the MIT Media Lab, where her thesis received the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing, and completed a postdoc at UC Berkeley. Her work has received Best Demo at NeurIPS, an honourable mention for Best Paper at ICML, Best of Collection in the IEEE Transactions on Affective Computing, and received several best paper awards at NeurIPS and AAAI workshops. She has interned at DeepMind, Google Brain, and was an OpenAI Scholars mentor. Her work has been featured in Science Magazine, MIT Technology Review, Quartz, IEEE Spectrum, Boston Magazine, and on CBC radio. Natasha earned her Masters degree from the University of British Columbia, and undergraduate degrees in Computer Science and Psychology from the University of Regina.

[@KshitizVermaDL]

Thank you Natasha for this awesome talk! I was looking an explanation to exactly the set of the papers chosen by you and I ended up watching your talk where you actually holistically compare them. Thanks a lot! This was super useful!

[@sudaravanm379]

Where can I get slides for the above presentation

[@CooperativeAIFoundation]

Here you go (with thanks to Natasha for sharing these): docs.google.com/presentation/d/1QyxGW2xCJNzzGtMqSWtLeT3u0PX_8d9p_e6oXdVcMgs/edit?usp=sharing

[@sudaravanm379]

@@CooperativeAIFoundation Thanks a lot!!!!