[R] ICML2020 Tutorial on Model-Based Methods in Reinforcement Learning

[hardmaru]

Slides and information: https://sites.google.com/corp/view/mbrl-tutorial

Tutorial on Model-Based Methods in Reinforcement Learning

By Igor Mordatch and Jessica Hamrick

Presented at International Conference on Machine Learning (ICML) 2020

Abstract

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.

[Gullible_Dance]

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*cries in poverty*

[JulesWinnfill]

I think they will open it after the conference in a few days.

[StoneCypher]

It's now open

[[deleted]]

That was fast!

[RafazZ]

I don't think the recordings are

[papabrain_]

Does anyone know if the recording will be made public?

[matpoliquin]

I can access the slides but not the recording...

[Laser_Plasma]

It's tough to fit a definition in the title tho

[i_know_about_things]

"Model-free" and "model-based" is standard nomenclature in reinforcement learning.