Here's a publicly-accessible link to the full paper: http://rdcu.be/cdlg
Only the first page, the rest is blurred and has to be paid for.
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Human-level control through deep reinforcement learning
21–30 of 63 posts
Re: Human-level control through deep reinforcement learning
#22Earlier quoted context omitted.
Only the first page, the rest is blurred and has to be paid for.
Let it load...
They are using tricks. When I tried the first time the pdf was blurred and the page automatically opened the payment menu. On the second try it showed the entire pdf, disallowing download.
Re: Human-level control through deep reinforcement learning
#23The code is online if you want to play with it. https://sites.google.com/a/deepmind.com/dqn/ If you're interested, one of the main authors (David Silver) teaches a very good and intuitive introductory class on reinforcement learning at UCL: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
Interesting that they're using Torch7. The code is pretty concise and readable, very cool stuff.
They wrote Torch7...
Re: Human-level control through deep reinforcement learning
#24I am wondering that what kind of real life problems could be modelled this way.
Re: Human-level control through deep reinforcement learning
#25The most interesting thing about this is that it shows significant progress towards goal-oriented AI. The fact this system is effectively learning what "win" means in the context of a game is something of a breakthrough.
I do not think that it figures out "what the win is" as the score parameter is explicitly made available to the algorithm.
In some sense this paper even demonstrates that how simple the problem actually is.
I think more important question is that what else can be modelled as such problem.
Re: Human-level control through deep reinforcement learning
#26Is there a chance this paper will be available as PDF? I' finding it difficult to read the readcube version. :-(
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Re: Human-level control through deep reinforcement learning
#27> ...the authors used the same algorithm, network architecture, and hyperparameters on each game... This is huge. It shows that the algorithm was able to generalize across multiple problem sets within the same domain of "playing Atari 2600 games", and not simply a "lucky" choice of algorithm, network architecture, or hyperparameters that a random search for each game might choose. This is also not a violation of the…
> the algorithm was able to generalize across multiple problem sets
Did it really? I think they reset it and retrained it for each game.
I'd like to know how much more is needed to make one instance of the AI that can successfully play any of the games. To play all 49 games that it could learn, does it need to be an extra level deep? Or 49 times larger? Or 2^49 times more?
Re: Human-level control through deep reinforcement learning
#28Earlier quoted context omitted.
Interesting that they're using Torch7. The code is pretty concise and readable, very cool stuff.
They wrote Torch7...
I'm sure a number of people who have contributed to Torch are working at DeepMind. However, Torch has been around for much longer than DeepMind (about 12 years at this point). Two of the major contributors to Torch, Ronan Collobert and Clement Farabet, were never DeepMind employees.
To be fair, another major contributor to Torch is a co-author of this paper (Kavukcuoglu).
Re: Human-level control through deep reinforcement learning
#29For comparison: http://www.cs.cmu.edu/~tom7/mario/. That is way more of a hack, but I am not sure this is that big a step forward. Space invaders and breakout aren't the hardest games and I haven't heard a hard argument that it is just a matter of scale to create a machine that, say, plays chess.
Re: Human-level control through deep reinforcement learning
#30Watch it play: http://www.nature.com/nature/journal/v518/n7540/extref/natur... http://www.nature.com/nature/journal/v518/n7540/extref/natur...
This is so cool. I'd love to work on this stuff...
Anyone know how hard it would be for someone who is fairly good at programming (works as a full stack developer and feels quite comfortable learning new things) and has strong math skills (undergrad degree) to break into this field? Is going back to school for a masters/phd the best way?