  Despite the recent progress in deep learning and reinforcement learning,
transfer and generalization of skills learned on specific tasks is very limited
compared to human (or animal) intelligence. The lifelong, incremental building
of common sense knowledge might be a necessary component on the way to achieve
more general intelligence. A promising direction is to build world models
capturing the true physical mechanisms hidden behind the sensorimotor
interaction with the environment. Here we explore the idea that inferring the
causal structure of the environment could benefit from well-chosen actions as
means to collect relevant interventional data.
