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Scale X_state down to [-1, 1] range in chapter 16

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Aurélien Geron 2017-11-09 16:35:44 +01:00
parent 7686839b36
commit 8a6c7da0a9
1 changed files with 2 additions and 2 deletions
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16_reinforcement_learning.ipynb
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@ -1410,7 +1410,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: the `preprocess_observation()` function is slightly different from the one in the book: instead of representing pixels as 64-bit floats from -1.0 to 1.0, it represents them as 8-bit integers from -128 to 127. The benefit is that the replay memory will take up about 6.5 GB of RAM instead of 52 GB. The reduced precision has no impact on training."
"Note: the `preprocess_observation()` function is slightly different from the one in the book: instead of representing pixels as 64-bit floats from -1.0 to 1.0, it represents them as signed bytes (from -128 to 127). The benefit is that the replay memory will take up roughly 8 times less RAM (about 6.5 GB instead of 52 GB). The reduced precision has no visible impact on training."
]
},
{
@ -1475,7 +1475,7 @@
"initializer = tf.contrib.layers.variance_scaling_initializer()\n",
"\n",
"def q_network(X_state, name):\n",
" prev_layer = X_state\n",
" prev_layer = X_state / 128.0 # scale pixel intensities to the [-1.0, 1.0] range.\n",
" with tf.variable_scope(name) as scope:\n",
" for n_maps, kernel_size, strides, padding, activation in zip(\n",
" conv_n_maps, conv_kernel_sizes, conv_strides,\n",