The project focuses on methodological contributions (models and algorithms) for training virtual and real agents to learn to solve complex tasks autonomously, targeting terrestrial mobile robots, typically service robots; industrial cobotics; autonomous vehicles; UAVs; humanoid robots. In particular, intelligent agents require high-level reasoning capabilities, situation awareness, and the capacity of robustly taking the right decisions at the right moments. The required behavior policies are complex, since they involve high-dimensional input spaces and state spaces, partially observed problems, as well as highly non-linear and entangled interdependencies. Learning them crucially depends on the algorithm’s capacity of learning compact, structured and semantically meaningful memory representations, which are able to capture short and long range regularities in the task and the environment. A second key requirement is the ability to learn these representations with a minimal amount of human interventions and annotations, as the manual design of complex representations is up to impossible. This requires the efficient usage of raw data through the discovery of regularities by different means: supervised, unsupervised or self-supervised learning, through reward or intrinsic motivation etc.