Deep Reinforcement Learning

author: David Silver, Department of Computer Science, University College London
published: July 28, 2015,   recorded: June 2015,   views: 7646
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

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.

See Also:

Download slides icon Download slides: rldm2015_silver_reinforcement_learning.pdf (2.3┬áMB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

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!

Write your own review or comment:

make sure you have javascript enabled or clear this field: