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Lately running into too many Sagemaker issues. Is there any unambiguous documentation on Sagemakers Instances? I could glean the following from different sources:
- Sagemaker Instances, Sagemaker being a managed service, have nothing to do with EC2 instances.
- Unlike EC2 console, Sagemaker console has no option to view limits or increase limits. One has to go directly to the support page a
In the updateEdgeStats function, reward is updated by edge.reward += reward, which is consistent with the formula in paper "Mastering the game of Go without human knowledge".
But in many other popular unofficial implementations, e.g. ,
, add
v to update the edge reward when the current node belongs to the current player, b
Documentation
首先感谢分享程序。
请问在6x6 四子棋的训练过程中,有没有调节learning_rate或者其他参数?
程序里的c_puct=5 , 温度t=1,学习率 0.002,batch_size 512 , deque最大长度10000, kl-targ=0.02 ,epochs=5
我使用你程序里的预设参数 tensorflow训练6x6 四子棋 ,loss下降到2左右就无法下降了,调节学习率也没成功。。。求帮助解答,谢谢
另外,不明白explain_var_old这个参考数值的意义。
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There are a bunch of links to the theory of NARs in the readme that link to a google site that is gone:
All links starting with: https://sites.google.com/site/narswang/
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At the moment if the digital twin model does not have the plugins built, we can not communicate them them thus we get the following error,
Timeout communicating with flight control plugin.
It would be helpful if the error message included a message reminding the user to check to make sure the plugin is built, or better yet do the check automatically.
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"scipy.misc.imsave" was removed in scipy version 1.3
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pip install scipy==1.2.1https://docs.scipy.org/doc/scipy/reference/release.1.3.0.html#scipy-interpolate-changes