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Model-Based Methods in Reinforcement Learning
Igor Mordatch · Jessica Hamrick

Mon Jul 13 08:00 AM -- 11:00 AM & Mon Jul 13 06:00 PM -- 09:00 PM (PDT) @ None

This tutorial presents a broad overview of the field of model-based reinforcement learning (MBRL), with a particular emphasis on deep methods. MBRL methods utilize a model of the environment to make decisions—as opposed to treating the environment as a black box—and present unique opportunities and challenges beyond model-free RL. We discuss methods for learning transition and reward models, ways in which those models can effectively be used to make better decisions, and the relationship between planning and learning. We also highlight ways that models of the world can be leveraged beyond the typical RL setting, and what insights might be drawn from human cognition when designing future MBRL systems.

Author Information

Igor Mordatch (Google Brain)
Jessica Hamrick (DeepMind)
Jessica Hamrick

Jessica Hamrick is a Senior Research Scientist at DeepMind, where she studies how to build machines that can flexibly build and deploy models of the world as well as humans. Her work combines insights from cognitive science with structured relational architectures, model-based deep reinforcement learning, and planning. Jessica received her Ph.D. in Psychology from UC Berkeley, and her M.Eng. in Computer Science and Engineering from MIT.

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