Our research field is Reinforcement Learning (RL) for robotics with different modes (e.g. language, sound, ...) and mechanisms (e.g. attention, curiosity, ...) and {task, skill, visual, knowledge} representations (e.g. embeddings, triples, ontology, graphs, ...). We make use of Neural Architecture Search (NAS) of medium to large Neural Networks (e.g. CNN, Transformer, ...). To benchmark, we evaluate our approach against implementations of methods from other authors to determine and compare the performance. We plan to apply explainability algorithms (e.g. Grad-CAM, RISE, SHAP, ... ) to check for plausibility of the agent's actions and reasoning.