During recent years, enormous advances in Reinforcement Learning (RL) have been showcased by DeepMind, OpenAI and others. Their AIs achieve similar-to-human or even super-human capabilities in various games including Atari games, the Chinese board game Go, Dota 2 and Starcraft. Although major Deep Learning frameworks reached new levels of maturity and usability when employing Supervised Learning, developing RL solutions often remains hard to get started and even harder to get right. In this talk we will look at the new TF-Agents framework simplifying development of RL solutions with TensorFlow 2.0 drastically. On the example of an AI playing Doom, we will present the relevant steps and code parts to get you started.