I will provide this formal answer that will point the reader to other potentially useful resources (including the one mentioned in a comment) with respect to the question.
- There's also this paid course Advanced AI: Deep Reinforcement Learning in Python, which seems to be completely dedicated to DRL. (Note that I have never followed this course, so I don't really know if it is good or not, i.e. I am just listing it for completeness).
In any case, if you are familiar with RL and deep learning topics, I encourage you to directly read the DQN papers (both by DeepMind folks)
Of course, deep RL isn't just DQN, but these are two very important papers that you should read.
You should also note that deep RL isn't anything special. It's just RL concepts combined with function approximators and deep learning tricks or deep neural networks. So, if you are really familiar with deep learning and RL (including the usage of neural networks to approximate policies and value functions), you don't need any course to understand deep RL concepts. You can just pick any deep RL paper and you can potentially understand it, although you may require more than 1-2 iterations (but that depends on the person).