[Computer-go] OpenAI Gym

Hi, OpenAI has released a "gym for reinforcement learning algorithms". It offers many environments, includes games etc., but also 9x9 Go and 19x19 Go. (Behind the scenes, it seems to use Pachi somehow. ;-) Maybe as a reference opponent.) https://gym.openai.com/envs#board_game

[Computer-go] Try a neural network in your browser

http://playground.tensorflow.org/ this actually uses your cpu to train the network it looks like you can either make a deep learning network with simple inputs or a shallow learning network with more complex inputs to solve the spiral case (the only challenging one) the shallower networks

Re: [Computer-go] Machine for Deep Neural Net training

You can also use hdf5 format, which has transparent compression as well as coffee support. Josef Dne st 27. 4. 2016 18:06 uživatel Gian-Carlo Pascutto napsal: > On 27-04-16 17:45, David Fotland wrote: > > I’d rather just buy another drive than spend time coding and > >

Re: [Computer-go] Machine for Deep Neural Net training

30M samples, 42 planes, 19x19 chars/plane, plus database overhead is 490 GB. In a dual boot machine that had windows on it originally, Windows wants to keep half of its original partition. I didn’t want to reinstall windows after formatting, so I have a 1 TB for Linux. However, AlphaGo

Re: [Computer-go] Machine for Deep Neural Net training

Looks like you're making good progress. Apart from the time gained training, you'll probably get a similar speed up when using the DNN during play? I'm curious when you'll see improvement in play outweigh the extra computational cost. Mark > On Apr 26, 2016, at 9:55 PM, David Fotland

Re: [Computer-go] Machine for Deep Neural Net training

What are you doing that uses so much disk space? An extremely naive computation of required space for what you are doing is: 30M samples * (42 input planes + 1 output plane)/sample * 19*19 floats/plane * 4 bytes/float = 1.7 TB So that's cutting it close, But I think the inputs and outputs are all