Learning intuitive physics and one-shot imitation using
  state-action-prediction self-organizing maps
  Human learning and intelligence work differently from the supervised pattern
recognition approach adopted in most deep learning architectures. Humans seem
to learn rich representations by exploration and imitation, build causal models
of the world, and use both to flexibly solve new tasks. We suggest a simple but
effective unsupervised model which develops such characteristics. The agent
learns to represent the dynamical physical properties of its environment by
intrinsically motivated exploration, and performs inference on this
representation to reach goals. For this, a set of self-organizing maps which
represent state-action pairs is combined with a causal model for sequence
prediction. The proposed system is evaluated in the cartpole environment. After
an initial phase of playful exploration, the agent can execute kinematic
simulations of the environment's future, and use those for action planning. We
demonstrate its performance on a set of several related, but different one-shot
imitation tasks, which the agent flexibly solves in an active inference style.
