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Human-level control through deep reinforcement learning

nature.com

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Re: Human-level control through deep reinforcement learning

#3
post #2

Is this a different paper to the original DeepMind video game paper? http://arxiv.org/abs/1312.5602

Yes, I can't access the full paper but at least the figures are different :)

Edit: Ars Technica has a summary of this new paper. https://arstechnica.com/science/2015/02/ai-masters-49-atari-...

Re: Human-level control through deep reinforcement learning

#7
> ...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 No Free Lunch (NFL) Theorem [1], because the domain is limited to playing Atari 2600 games, which share many characteristics.

[1]: https://en.wikipedia.org/wiki/No_free_lunch_in_search_and_op...

Re: Human-level control through deep reinforcement learning

#9
post #8

Can someone convert "academia nerd language" down one notch into "regular nerd language". On the surface, this sounds interesting but despite being a huge nerd I'm not really sure what the hell they're talking about.

They built a system that automatically learned to play 49 different games, without any human tweaking (aside from the original setup).
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