import tensorflow as tf

            self.R = tf.placeholder(tf.float32, [None, ], name='R')  # input Reward
            self.a = tf.placeholder(tf.float32, [None, self.num_a], name='a')  # input Action onehot for agent1
            self.done = tf.placeholder(tf.float32, [None, ], name='done')  # input Done info ???

            self.q_m_ = tf.placeholder(tf.float32, [None, ], name='q_value_next_max')
            self.q_target = tf.placeholder(tf.float32, [None,], name='q_tot_target')

            w_initializer, b_initializer = tf.random_normal_initializer(0., 0.1), tf.constant_initializer(0.0)

            # ------------------ build evaluate_net ------------------
            with tf.variable_scope('eval_net'):
                a_fc1 = tf.layers.dense(self.s, 128, tf.nn.relu, kernel_initializer=w_initializer,
                                        bias_initializer=b_initializer, name='agent_fc1_e')
                # a_fc2 = tf.layers.dense(a_fc1, 128, tf.nn.relu, kernel_initializer=w_initializer,
