def build_main_model(self):
"""
Builds neural network model to approximate policy and value functions
"""
if self.parameters['obs_type'] == 'image':
self.observation = tf.placeholder(shape=[
None, self.parameters["frame_height"],
self.parameters["frame_width"], self.parameters["num_frames"]
],
dtype=tf.float32,
name='observation')
else:
self.observation = tf.placeholder(
shape=[None, self.parameters['obs_size']],
dtype=tf.float32,
name='observation')
# normalize input
if self.parameters['env_type'] == 'atari':
self.observation = tf.cast(self.observation, tf.float32) / 255.
if self.convolutional:
self.feature_vector = net.cnn(self.observation,
self.parameters["num_conv_layers"],
self.parameters["num_filters"],
self.parameters["kernel_sizes"],
self.parameters["strides"],
tf.nn.relu, False, 'cnn')
network_input = c_layers.flatten(self.feature_vector)
else:
self.feature_vector = self.observation
network_input = self.feature_vector
if self.fully_connected:
hidden = net.fcn(network_input, self.parameters["num_fc_layers"],
self.parameters["num_fc_units"], tf.nn.relu,
'fcn')
if self.recurrent:
self.prev_action = tf.placeholder(shape=[None],
dtype=tf.int32,
name='prev_action')
self.prev_action_onehot = c_layers.one_hot_encoding(
self.prev_action, self.act_size)
# network_input = tf.concat([network_input, self.prev_action_onehot], axis=1)
c_in = tf.placeholder(tf.float32,
[None, self.parameters['num_rec_units']],
name='c_state')
h_in = tf.placeholder(tf.float32,
[None, self.parameters['num_rec_units']],
name='h_state')
self.seq_len = tf.placeholder(shape=None,
dtype=tf.int32,
name='sequence_length')
self.state_in = tf.contrib.rnn.LSTMStateTuple(c_in, h_in)
hidden, self.state_out = net.rnn(network_input, self.state_in,
self.parameters['num_rec_units'],
self.seq_len, 'rnn')
self.hidden = hidden
