def cnn_v0(state,
num_actions,
scope,
channels=32,
activation_fn=None,
is_training=True,
reuse=False,
use_timestep=True):
"""CNN architecture for discrete-output DQN.
Args:
state: 2-Tuple of image and timestep tensors: (image, timestep).
num_actions: (int) Number of discrete actions.
scope: String name of the TF variable scope.
channels: Number of channels in each layer.
activation_fn: Python function specifying activation of final layer. Can be
used to implement action clipping for DDPG Actors.
is_training: Whether this graph is for training or inference.
reuse: Whether or not to reuse variables from this variable scope.
use_timestep: If True, incorporate timestep into model prediction.
Returns:
Tensor of size (batch_size, num_actions) representing Q(s, a) for each
action.
"""
if use_timestep:
net, timestep = state
else:
net = state
end_points = {}
with tf.variable_scope(scope, reuse=reuse, use_resource=True):
with slim.arg_scope(tf_modules.argscope(is_training=is_training)):
for layer_index in range(3):
net = slim.conv2d(net, channels, kernel_size=3)
logging.info('conv%d %s', layer_index, net.get_shape())
if use_timestep:
_, height, width, _ = net.get_shape().as_list()
timestep = tf.cast(timestep, tf.float32)
timestep = tf.tile(tf.reshape(timestep, [-1, 1, 1, 1]),
[1, height, width, 1])
net = tf.concat([net, timestep], axis=3)
net = tf.layers.flatten(net)
net = slim.stack(net, slim.fully_connected, [channels, channels])
net = slim.fully_connected(net,
num_outputs=num_actions,
normalizer_fn=None,
weights_regularizer=None,
activation_fn=activation_fn)
return net, end_points
def cnn_ia_v1(state,
action,
scope,
channels=32,
num_convs=3,
is_training=True,
reuse=False,
use_timestep=True):
"""CNN architecture for DQN that takes action as vector-encoded input.
Args:
state: 2-Tuple of image and timestep tensors: (image, timestep).
action: Tensor of proposed actions.
scope: String name of the TF variable scope.
channels: Number of channels in each layer.
num_convs: Number of convolution channels to apply to the state.
is_training: Whether this graph is for training or inference.
reuse: Whether or not to reuse variables from this variable scope.
use_timestep: If True, incorporate timestep into model prediction.
Returns:
Tensor of size (batch_size,) representing Q(s, a).
"""
net, timestep = state
end_points = {}
with tf.variable_scope(scope, reuse=reuse, use_resource=True):
with slim.arg_scope(tf_modules.argscope(is_training=is_training)):
for layer_index in range(num_convs):
net = slim.conv2d(net, channels, kernel_size=3)
logging.info('conv%d %s', layer_index, net.get_shape())
if use_timestep:
_, height, width, _ = net.get_shape().as_list()
timestep = tf.cast(timestep, tf.float32)
timestep = tf.tile(tf.reshape(timestep, [-1, 1, 1, 1]),
[1, height, width, 1])
net = tf.concat([net, timestep], axis=3)
# Process Action
context = slim.fully_connected(action, channels + 1)
else:
context = slim.fully_connected(action, channels)
net = tf_modules.add_context(net, context)
net = tf.layers.flatten(net)
net = slim.stack(net, slim.fully_connected, [channels, channels])
net = slim.fully_connected(net,
num_outputs=1,
normalizer_fn=None,
weights_regularizer=None,
activation_fn=None)
return tf.squeeze(net, 1), end_points
