Playing Atari with Deep Reinforcement Learning

The rules of Go are not exactly "programmed in" to AlphaGo; they are only provided so the model knows what the valid moves and win conditions are as it self-trains. The latest versions of the model can be quickly adapted to play other games[0] and MuZero can "master games…

gary_0Jan 21, 2024View on HNParent context

A lot depends on what you're interested in. Some papers that are runnable on a laptop CPU (so long as you stick to small image sizes/tasks): 1) Generative Adversarial Networks (https://arxiv.org/abs/1406.2661). Good practice to have a custom training loops, different optimisers…

phonebucketJan 24, 2023View on HNParent context

PLAGIARISM Third paragraph of Intro in original DeepMind paper https://arxiv.org/pdf/1312.5602.pdf & Second paragraph of Intro(5th line) of this project. http://nihit.github.io/resources/spaceinvaders.pdf They didn't even bother to do something original except for using the RAM…

ap46Apr 11, 2017View on HNParent context

Looks really cool. I recently hit a roadblock when trying to implement the original DeepMind Atari algorithm [0] with TensorFlow. They don't mention this in the paper, but the network wasn't trained to convergence at each training step (maybe this would be obvious to people more…

komaromyJan 3, 2017View on HNParent context

> once we have a comprehensive AI program that can play many kinds of games Before AlphaGo, DeepMind released a reinforcement learning algorithm that could play many Atari games just from the raw pixels on the screen, in many games surpassing humans. The same algorithm.…

visargaAug 19, 2016View on HNParent context

I find it really interesting that Goal 4 is a game playing agent. Deepmind has been focussing on this since the beginning[0] and actually has great progress as far as Atari games go[1]. And DeepMind is able to use a single agent rather than a different specific one for each…

kttaJun 20, 2016View on HNParent context

The examples where this happens have always seemed fairly weak to me. How many of the grave errors, not just where it's the wrong type of animal or container but actually thinking it's radically different, survive an application of Gaussian blur? Furthermore self-driving cars…

jkldotioMay 10, 2016View on HNParent context

Raw pixels. And the score. The score was separate, or rather a signal representing increased score. Relevant quote from the paper: "The emulator’s internal state is not observed by the agent; instead it observes an image xt ∈ Rd from the emulator, which is a vector of raw pixel…

daveguyMar 21, 2016View on HNParent context

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

nlFeb 25, 2015View on HNParent context

PDF: http://arxiv.org/pdf/1312.5602v1.pdf

sharemywinFeb 26, 2015View on HNParent context

Currently, it sees a board of 3x3 states, as in: _ _ G G B B _ _ G A next step would be to use real computer vision over a 3D camera in the environment aligned to R2D@ and a deep neural network to interpret pixels instead of states. DeepMind has a paper describing this approach…

javierluraschiFeb 25, 2015View on HNParent context

The speech recognition and synthesis are primitive compared to any smartphone assistant these days. The natural language processing is about equivalent to SHRDLU from the 1960s [1]. It turns out that this approach based on manually constructing syntax trees and applying simple…

modelessJan 18, 2015View on HNParent context

Here's the original research paper if you're interested. http://arxiv.org/abs/1312.5602 I'll just quote their introduction instead of trying to summarize the paper: "Our goal is to create a single neural network agent that is able to successfully learn to play as many of the…

amorusoDec 2, 2014View on HNParent context

While this complaint generally has validity, their paper [1] does IMO present an advance; it's not just handing a bunch of labeled data off to a large neural network. IIRC (forgive me, I read the paper a few weeks ago) the solution is at its core a reinforcement learning system…

sinwaveDec 3, 2014View on HNParent context

A note -- if you're linking to arXiv, it's better to link to the abstract (http://arxiv.org/abs/1312.5602) rather than directly to the PDF. From the abstract, one can easily click through to the PDF; not so the reverse. And the abstract allows one to do things like see different…

SniffnoyOct 21, 2014View on HNParent context

I like this one because it is easier to get an idea of what the layout of the neural network itself is, and gives more control. Off the topic of neural networks in javascript but on the topic of neural networks in general, the readme links a cool paper on applying convolutional…

cyorirJul 11, 2014View on HNParent context

It's worth pointing out Google just bought the startup with the guys who wrote the "Playing Atari with Deep Reinforcement Learning" paper which is supposedly just an algorithm working on the raw pixels and then beating all other programs and half of the human experts.[1] [1]…

jkldotioFeb 11, 2014View on HNParent context

beating the 2600 via AI is worth $500M, so beating Nintendo should be worth around $2B. http://arxiv.org/pdf/1312.5602v1.pdf

jgalt212Jan 28, 2014View on HNParent context

I remember that they had an impressive demo at the NIPS Deep Learning Workshop in December (probably a highlight for me and a few people I talked to afterwards) of a reinforcement learning agent playing Atari 2600 Games where the input consisted of an image that was fed through…

karpathyJan 27, 2014View on HNParent context

Exactly. Interesting, Yann LeCun in his G+ post (https://plus.google.com/u/0/+YannLeCunPhD/posts/LnKBPi9cCWm) explicitly mentioned that the acquired ML scientists have been focused in reinforcement learning. Also, they have published ArXiv paper on the subject…

amaksJan 27, 2014View on HNParent context