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

Intrinsic Motivation Driven Intuitive Physics Learning using Deep Reinforcement Learning with Intrinsic Reward Normalization