Arthur Juliani awjuliani
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| class AC_Network(): | |
| def __init__(self,s_size,a_size,scope,trainer): | |
| .... | |
| .... | |
| .... | |
| if scope != 'global': | |
| self.actions = tf.placeholder(shape=[None],dtype=tf.int32) | |
| self.actions_onehot = tf.one_hot(self.actions,a_size,dtype=tf.float32) | |
| self.target_v = tf.placeholder(shape=[None],dtype=tf.float32) | |
| self.advantages = tf.placeholder(shape=[None],dtype=tf.float32) |
awjuliani
/ Q-Exploration.ipynb
Created
November 14, 2016 21:57
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awjuliani
/ ContextualPolicy.ipynb
Last active
October 11, 2022 21:27
A Policy-Gradient algorithm that solves Contextual Bandit problems.
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awjuliani
/ Q-Table Learning-Clean.ipynb
Last active
October 25, 2022 07:57
Q-Table learning in OpenAI grid world.
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awjuliani
/ SimplePolicy.ipynb
Created
September 11, 2016 00:20
Policy gradient method for solving n-armed bandit problems.
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awjuliani
/ Deep-Recurrent-Q-Network.ipynb
Last active
July 18, 2023 19:18
An implementation of a Deep Recurrent Q-Network in Tensorflow.
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awjuliani
/ Q-Net Learning Clean.ipynb
Created
August 25, 2016 20:30
Basic Q-Learning algorithm using Tensorflow
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