Lecture

Mini-Batches in On- and Off-Policy Deep Reinforcement Learning

In course

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Description

This lecture covers the importance of mini-batches in Deep Reinforcement Learning, explaining how to avoid data correlation by using replay buffers or multiple actors. It discusses on-policy and off-policy methods, such as Q-Learning and Advantage Actor-Critic, and the pros and cons of each approach.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_iwfj0k6g

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