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3) You can look at OpenAI baseline or something like this(https://github.com/DLR-RM/stable-baselines3) to make sure the results are reproducible.
When I started my master thesis last year I was a complete noob in ML, let alone RL. I tried to search for some code I could finally understand and stumbled upon this pretty nice notebook: https://github.com/fg91/Deep-Q-Learning It's by far the best notebook I've worked with yet. Since my goal was to learn PyTorch instead of Tensorflow (used in the notebook, it's also not working properly without tweaks due an old version of TF), I started re-implementing the code in PyTorch. Good thing is that you can compare your own results to the notebook and debug everything with prints if needed. That way I learned a lot about PyTorch and DQN.
This series is actually easier than the David Silver one. Also here is the github repository link - https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch