Deep Reinforcement Learning

author: David Silver, Department of Computer Science, University College London
published: July 28, 2015,   recorded: June 2015,   views: 14065


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In this tutorial I will discuss how reinforcement learning (RL) can be combined with deep learning (DL). There are several ways to combine DL and RL together, including value-based, policy-based, and model-based approaches with planning. Several of these approaches have well-known divergence issues, and I will present simple methods for addressing these instabilities. The talk will include a case study of recent successes in the Atari 2600 domain, where a single agent can learn to play many different games directly from raw pixel input.

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Reviews and comments:

Comment1 Alan Flynn, May 24, 2016 at 4:10 p.m.:

Great speed! The interruption every couple of seconds really give me the time to think about the material!

Comment2 Luc, August 14, 2018 at 3:21 a.m.:

And just like in David's lectures, his laptop periodically shuts off because he forgot to plug it in. Brilliant.

Absolutely amazing talk, in any case.

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Comment4 wapojadit atech, May 29, 2019 at 12:36 p.m.:

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