import tensorflow as tf

		lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(RNN_SIZE,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)
		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])
		state_in = (c_in, h_in)
		rnn_in = tf.expand_dims(self.h3, [0])
		step_size = tf.shape(inputs)[: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
		state_out = (lstm_c[:1, :], lstm_h[:1, :])
		self.rnn_out = tf.reshape(lstm_outputs, [-1, RNN_SIZE])

		'''
		CHANGES
		- removed blocking layer
		- edited out stop_gradient lines (Dont need them)
		'''
		# Value FC
		value         = Dense(units=1, kernel_initializer=normalized_columns_initializer(1.0), bias_initializer=None, activation=None)(inputs=self.rnn_out)

		# rnn_out_frozen = tf.stop_gradient(self.rnn_out)
		next_loc_mean  = Dense(units=2, kernel_initializer=normalized_columns_initializer(1.0), bias_initializer=None, activation=tf.math.tanh)(inputs=self.rnn_out)	# was rnn_out_frozen
		loc_std        = Dense(units=1, kernel_initializer=normalized_columns_initializer(1.0), activation=tf.nn.softplus)(inputs = self.rnn_out)

		# Policy FC
