I am new in reinforcement learning, but I already know deep Q-learning and Q-learning. Now, I want to learn about double deep Q-learning.

Do you know any good references for double deep Q-learning?

I have read some articles, but some of them don't mention what the loss is and how to calculate it, so many articles are not complete. Also, Sutton and Barto (in their book) don't describe that algorithm either.

Please, help me to learn Double Q-learning.


2 Answers 2


If you're interested in the theory behind Double Q-learning (not deep!), the reference paper would be Double Q-learning by Hado van Hasselt (2010).

As for Double deep Q-learning (also called DDQN, short for Double Deep Q-networks), the reference paper would be Deep Reinforcement Learning with Double Q-learning by Van Hasselt et al. (2016), as pointed out in ddaedalus's answer.

As for how the loss is calculated, it is not explicitly written in the paper. But, you can find it in the Dueling DQN paper, which is a subsequent paper where Van Hasselt is a coauthor. In the appendix, the authors provide the pseudocode for Double DQN. The relevant part for you would be:

$y_{j}=\left\{\begin{array}{ll}r & \text { if } s^{\prime} \text { is terminal } \\ r+\gamma Q\left(s^{\prime}, a^{\max }\left(s^{\prime} ; \theta\right) ; \theta^{-}\right), & \text {otherwise}\end{array}\right.$

Do a gradient descent step with loss $ \left\|y_{j}-Q(s, a ; \theta)\right\|^{2}$

Here, $y_j$ is the target, $\theta$ are the parameters of the regular network and $\theta^{-}$ are the target network parameters.

The most important thing to note here is the difference with the DQN target: $y_{i}^{D Q N}=r+\gamma \max _{a^{\prime}} Q\left(s^{\prime}, a^{\prime} ; \theta^{-}\right)$.

In DQN, we evaluate the Q-values based on parameters $\theta^{-}$ and we take the max over actions based on these Q-values parametrized with the same $\theta^{-}$. The problem with this is that it leads to an overestimation bias, especially at the beginning of the training process, where the Q-values estimates are noisy.

In order to address this issue, in double DQN, we instead take the max based on Q-values calculated using $\theta$ and we evaluate the Q-value of $a^{max}$ based on a different set of parameters i.e. $\theta^{-}$.

If you want to learn more about this, by watching a video lecture instead of reading a paper, I'd suggest you take a look at this lecture from UC Berkley's DRL course, where the professor (Sergey Levine) discusses this in detail with examples.


You should first read the introductory paper of Double DQN.


Then, depending on what you would like to do, search for other relevant papers that use this method.

I also propose studying the original Double Q-learning paper to understand important concepts/issues, such as the overestimation bias of Q-learning: https://proceedings.neurips.cc/paper/2010/file/091d584fced301b442654dd8c23b3fc9-Paper.pdf


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