| class AC_Network(): | |
| def __init__(self,s_size,a_size,scope,trainer): | |
| with tf.variable_scope(scope): | |
| #Input and visual encoding layers | |
| self.inputs = tf.placeholder(shape=[None,s_size],dtype=tf.float32) | |
| self.imageIn = tf.reshape(self.inputs,shape=[-1,84,84,1]) | |
| self.conv1 = slim.conv2d(activation_fn=tf.nn.elu, | |
| inputs=self.imageIn,num_outputs=16, | |
| kernel_size=[8,8],stride=[4,4],padding='VALID') | |
| self.conv2 = slim.conv2d(activation_fn=tf.nn.elu, | |
| inputs=self.conv1,num_outputs=32, | |
| kernel_size=[4,4],stride=[2,2],padding='VALID') | |
| hidden = slim.fully_connected(slim.flatten(self.conv2),256,activation_fn=tf.nn.elu) | |
| #Recurrent network for temporal dependencies | |
| lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(256,state_is_tuple=True) | |
| c_init = np.zeros((1, lstm_cell.state_size.c), np.float32) | |
| h_init = np.zeros((1, lstm_cell.state_size.h), np.float32) | |
| self.state_init = [c_init, h_init] | |
| c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c]) | |
| h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h]) | |
| self.state_in = (c_in, h_in) | |
| rnn_in = tf.expand_dims(hidden, [0]) | |
| step_size = tf.shape(self.imageIn)[:1] | |
| state_in = tf.nn.rnn_cell.LSTMStateTuple(c_in, h_in) | |
| lstm_outputs, lstm_state = tf.nn.dynamic_rnn( | |
| lstm_cell, rnn_in, initial_state=state_in, sequence_length=step_size, | |
| time_major=False) | |
| lstm_c, lstm_h = lstm_state | |
| self.state_out = (lstm_c[:1, :], lstm_h[:1, :]) | |
| rnn_out = tf.reshape(lstm_outputs, [-1, 256]) | |
| #Output layers for policy and value estimations | |
| self.policy = slim.fully_connected(rnn_out,a_size, | |
| activation_fn=tf.nn.softmax, | |
| weights_initializer=normalized_columns_initializer(0.01), | |
| biases_initializer=None) | |
| self.value = slim.fully_connected(rnn_out,1, | |
| activation_fn=None, | |
| weights_initializer=normalized_columns_initializer(1.0), | |
| biases_initializer=None) |
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