  At an early age, human infants are able to learn and build a model of the
world very quickly by constantly observing and interacting with objects around
them. One of the most fundamental intuitions human infants acquire is intuitive
physics. Human infants learn and develop these models, which later serve as
prior knowledge for further learning. Inspired by such behaviors exhibited by
human infants, we introduce a graphical physics network integrated with deep
reinforcement learning. Specifically, we introduce an intrinsic reward
normalization method that allows our agent to efficiently choose actions that
can improve its intuitive physics model the most.
  Using a 3D physics engine, we show that our graphical physics network is able
to infer object's positions and velocities very effectively, and our deep
reinforcement learning network encourages an agent to improve its model by
making it continuously interact with objects only using intrinsic motivation.
We experiment our model in both stationary and non-stationary state problems
and show benefits of our approach in terms of the number of different actions
the agent performs and the accuracy of agent's intuition model.
  Videos are at https://www.youtube.com/watch?v=pDbByp91r3M&t=2s
