continuous_deepq.py 文件源码

def update_target_network(source_network, target_network, update_rate):
        target_network_update = []
        for v in source_network.variables():
            # this is equivalent to target = (1-alpha) * target + alpha * source
            # print ("source: " + v.name + " : " + str(v.get_shape()))
            pass
        for v in target_network.variables():
            # this is equivalent to target = (1-alpha) * target + alpha * source
            # print ("target: " + v.name + " : " + str(v.get_shape()))
            pass
        for v_source, v_target in zip(source_network.variables(), target_network.variables()):
            # this is equivalent to target = (1-alpha) * target + alpha * source
            update_op = v_target.assign_sub(update_rate * (v_target - v_source))
            target_network_update.append(update_op)
        return tf.group(*target_network_update)

#    def concat_nn_input(self, input1, input2):
#        return tf.concat(1, [input1, input2])

#    def add_pow_values(self, values):
#        return self.concat_nn_input(values, 0.01 * tf.pow(values, [2 for i in range(self.action_size)]))