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Reinforcement learning seems like overkill if you're just trying to match a single target. RL agents want to maximize reward accumulated over time, while you'd

by davmre 11y ago

Reinforcement learning seems like overkill if you're just trying to match a single target. RL agents want to maximize reward accumulated over time, while you'd be happy to find a single good state. You could frame this as stochastic optimization (MCMC or simulated annealing) over the CSS string, with some combination of RNN and convnet serving as proposal. The best paper award at CVPR this year (http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Kulkarni_Picture_A_Probabilistic_2015_CVPR_paper.html http://www.cv-foundation.org/openaccess/content_cvpr_2015/ht...) has an architecture somewhat like this: instead of a web browser they're using a 3D rendering engine, and searching for pose parameters that cause their rendered image to match an observed image. This is just Bayesian inference using MCMC, where they train a deep net to function as a data-driven proposal distribution. If your goal were to actually get a working system, you'd probably want to do inference directly on a parameter vector encoding all the relevant quantities - heights, widths, font sizes, colors, etc. of the various boxes, etc. - that you could programmatically ground out into a CSS file. Trying to do inference over the raw text is making things artificially hard since you have to put so much work into even just getting correct syntax. Though maybe that's part of the fun. :-)

Houshalter 11y ago
I think the optimal way to do this would be to use an RNN that can walk up and down the DOM. So it starts at the HTML tag, and chooses which path to go up and down, visiting each node. Then iterate through every style attribute and predict what value it should have. This would create a simple way of generating CSS styles for a document, without dealing with the complicated issues of RNNs having limited memory and producing correct syntax. Then you can use these predictions as a prior probability over what the CSS styles should be. Then you can use some kind of bayesian optimization to find the optimal settings in the least number of experiments.

by davmre 11y ago

Houshalter 11y ago

I think the optimal way to do this would be to use an RNN that can walk up and down the DOM. So it starts at the HTML tag, and chooses which path to go up and down, visiting each node. Then iterate through every style attribute and predict what value it should have. This would create a simple way of generating CSS styles for a document, without dealing with the complicated issues of RNNs having limited memory and producing correct syntax. Then you can use these predictions as a prior probability over what the CSS styles should be. Then you can use some kind of bayesian optimization to find the optimal settings in the least number of experiments.