Explore a comprehensive lecture on practical model-based algorithms for reinforcement learning and imitation learning presented by Tengyu Ma from Stanford University. Delve into topics such as sample efficiency, model-based reinforcement learning challenges, learning dynamics, and dealing with uncertainty. Examine ideal loss, expectations, upper bounds, and demonstrations through examples. Gain insights into evaluation methods and discover open questions in the field. This 48-minute talk, part of the Frontiers of Deep Learning series at the Simons Institute, offers valuable knowledge for researchers and practitioners in the field of artificial intelligence and machine learning.
Practical Model-Based Algorithms for Reinforcement Learning and Imitation Learning
Overview
Syllabus
Intro
Sample Efficiency
Reinforcement Learning
ModelbasedReinforcement Learning
Challenges
Learning dynamics
Autism in the face of uncertainty
Examples
Ideal Loss
Expectations
Upper Bound
Demonstration
Evaluation
Open Questions
Taught by
Simons Institute
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