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@awjuliani
Last active October 11, 2022 21:27
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A Policy-Gradient algorithm that solves Contextual Bandit problems.
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@pooriaPoorsarvi
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can anyone explain to me why we do not use softmax instead of sigmoid? and also why we don't use bias?(I tried both and it wouldn't work)

@pooriaPoorsarvi
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@lipixun do you know the answer to my question? it would really help me thanks

@JaeDukSeo
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@pooriaPoorsarvi as seen above we already got the responsible_weight variable, now we are getting the negative
Log likelihood to optimize for the maxium (tf only can optimize) no need to consider every other classes

@araknadash
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Instead of using slim, can use tf as:
state_in_OH = tf.one_hot(self.state_in, s_size)
output = tf.layers.dense(state_in_OH, a_size, tf.nn.sigmoid, use_bias=False, kernel_initializer = tf.ones_initializer())

@xkrishnam
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xkrishnam commented Jul 29, 2020

Thanks Arthur! this is helpful tutorial for beginers like me. Here is tensorflow 2 implementation may be helpful for someone

@daniel-xion
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Thanks Arthur! this is helpful tutorial for beginers like me. Here is tensorflow 2 implementation may be helpful for someone

Thanks for the implementation. I wonder how is the implementation a policy network? I don't see policy gradient is used.

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