import tensorflow as tf

            # state and target
            self.state = tf.placeholder(tf.float32, [None,num_state], "state")
            self.target = tf.placeholder(tf.float32, [None,1], name="target")

            # layers
            l_c = tf.layers.dense(self.state, 100, tf.nn.relu6, kernel_initializer=w_init, name='lc')
            self.value_estimate = tf.layers.dense(l_c, 1, kernel_initializer=w_init, name='v')  # estimated value for state
        
            # loss and optimizer
            self.loss = tf.reduce_mean(tf.square(tf.subtract(self.value_estimate, self.target)))
            self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
            self.train_op = self.optimizer.minimize(
