Stefan_Haerter:

But where in this tutorial is the agent located?
[Thank you very much for your time and sorry for such maybe dumb questions]


No problem. Deep RL is not easy.
Your agent [an abstract term, so it’s just your program] would try to learn to do X by interacting with an environment (e.g. the game of Pong via Gym or an Android app via AndroidEnv using a policy (approximated by a neural net - hence “deep” RL) to gain experience. The policy (your neural net) “belongs” to your agent - it maps the agent’s observations (inputs, could be image pixels or sequential data directly via an API) to its actions/action log probabilities (outputs).
In addition, you may find this post - Deep Reinforcement Learning With TensorFlow 2.1 | Roman Ring 1 - helpful. It was written by an engineer who now works at DeepMind.  In addition, there’s a YouTube channel that teaches well-known "basic deep RL methods - such as DQN, policy gradients, and actor-critic methods - with core TensorFlow 2. Check out Everything You Need To Master Actor Critic Methods | Tensorflow 2 Tutorial - YouTube 1 (actor-critic methods) or Deep Q Learning With Tensorflow 2 - YouTube 1 (DQN).