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Parallel agents fixes (#95)

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* Parallel agents related bug fixes: checkpoint restore, tensorboard integration.
Adding narrow networks support.
Reference code for unlimited number of checkpoints
This commit is contained in:
Itai Caspi
2018-05-24 14:24:19 +03:00
committed by GitHub
parent 6c0b59b4de
commit d302168c8c
10 changed files with 75 additions and 41 deletions
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36
architectures/tensorflow_components/embedders.py
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@@ -15,18 +15,20 @@
#
import tensorflow as tf
from configurations import EmbedderComplexity
from configurations import EmbedderDepth, EmbedderWidth
class InputEmbedder(object):
def __init__(self, input_size, activation_function=tf.nn.relu,
embedder_complexity=EmbedderComplexity.Shallow, name="embedder"):
embedder_depth=EmbedderDepth.Shallow, embedder_width=EmbedderWidth.Wide,
name="embedder"):
self.name = name
self.input_size = input_size
self.activation_function = activation_function
self.input = None
self.output = None
self.embedder_complexity = embedder_complexity
self.embedder_depth = embedder_depth
self.embedder_width = embedder_width
def __call__(self, prev_input_placeholder=None):
with tf.variable_scope(self.get_name()):
@@ -47,15 +49,16 @@ class InputEmbedder(object):
class ImageEmbedder(InputEmbedder):
def __init__(self, input_size, input_rescaler=255.0, activation_function=tf.nn.relu,
embedder_complexity=EmbedderComplexity.Shallow, name="embedder"):
InputEmbedder.__init__(self, input_size, activation_function, embedder_complexity, name)
embedder_depth=EmbedderDepth.Shallow, embedder_width=EmbedderWidth.Wide,
name="embedder"):
InputEmbedder.__init__(self, input_size, activation_function, embedder_depth, embedder_width, name)
self.input_rescaler = input_rescaler
def _build_module(self):
# image observation
rescaled_observation_stack = self.input / self.input_rescaler
if self.embedder_complexity == EmbedderComplexity.Shallow:
if self.embedder_depth == EmbedderDepth.Shallow:
# same embedder as used in the original DQN paper
self.observation_conv1 = tf.layers.conv2d(rescaled_observation_stack,
filters=32, kernel_size=(8, 8), strides=(4, 4),
@@ -73,7 +76,7 @@ class ImageEmbedder(InputEmbedder):
self.output = tf.contrib.layers.flatten(self.observation_conv3)
elif self.embedder_complexity == EmbedderComplexity.Deep:
elif self.embedder_depth == EmbedderDepth.Deep:
# the embedder used in the CARLA papers
self.observation_conv1 = tf.layers.conv2d(rescaled_observation_stack,
filters=32, kernel_size=(5, 5), strides=(2, 2),
@@ -115,24 +118,27 @@ class ImageEmbedder(InputEmbedder):
class VectorEmbedder(InputEmbedder):
def __init__(self, input_size, activation_function=tf.nn.relu,
embedder_complexity=EmbedderComplexity.Shallow, name="embedder"):
InputEmbedder.__init__(self, input_size, activation_function, embedder_complexity, name)
embedder_depth=EmbedderDepth.Shallow, embedder_width=EmbedderWidth.Wide,
name="embedder"):
InputEmbedder.__init__(self, input_size, activation_function, embedder_depth, embedder_width, name)
def _build_module(self):
# vector observation
input_layer = tf.contrib.layers.flatten(self.input)
if self.embedder_complexity == EmbedderComplexity.Shallow:
self.output = tf.layers.dense(input_layer, 256, activation=self.activation_function,
width = 128 if self.embedder_width == EmbedderWidth.Wide else 32
if self.embedder_depth == EmbedderDepth.Shallow:
self.output = tf.layers.dense(input_layer, 2*width, activation=self.activation_function,
name='fc1')
elif self.embedder_complexity == EmbedderComplexity.Deep:
elif self.embedder_depth == EmbedderDepth.Deep:
# the embedder used in the CARLA papers
self.observation_fc1 = tf.layers.dense(input_layer, 128, activation=self.activation_function,
self.observation_fc1 = tf.layers.dense(input_layer, width, activation=self.activation_function,
name='fc1')
self.observation_fc2 = tf.layers.dense(self.observation_fc1, 128, activation=self.activation_function,
self.observation_fc2 = tf.layers.dense(self.observation_fc1, width, activation=self.activation_function,
name='fc2')
self.output = tf.layers.dense(self.observation_fc2, 128, activation=self.activation_function,
self.output = tf.layers.dense(self.observation_fc2, width, activation=self.activation_function,
name='fc3')
else:
raise ValueError("The defined embedder complexity value is invalid")