# Why are Dueling Q Networks not used more often to approximate Q-values in reinforcement learning algorithms?

I've just learned about Dueling Network Architectures to estimate $$Q$$-values and am wondering why this architecture is not used more often in deep RL algorithms? DDPG and TD3 estimate the $$Q$$-function using Double Q Learning instead of the empirically better Dueling Approach.