Offline RL: learning to make decisions directly from datasets
Details
Reinforcement Learning is a fast-growing field that is starting to make an impact across different engineering areas. However, Reinforcement Learning is typically framed as an Online Learning approach where an Environment (simulated or real) is required during the learning process.
The need of an environment is typically a constrain that prevents the application of RL techniques in fields where having a simulator is very hard or unfeasible (e.g., Health, NLP, etc.).
In this talk, we will show how to apply Reinforcement Learning to ML problems where the only available resource is a Dataset, i.e., a recording of interactions of an Agent in an Environment.
Our speaker, Edilmo Palencia, is Principal AI Engineer at the Autonomous Systems division of Microsoft. There, he is part of the AI Team behind Project Bonsai: a low-code/no-code AI platform that speeds the creation of AI-powered automation.
Outside of Microsoft Edilmo also works in Computational Linguistics and is part of an ONG called “Code for Venezuela”.
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