  Recent findings suggest that humans deploy cognitive mechanism of physics
simulation engines to simulate the physics of objects. We propose a framework
for bots to deploy probabilistic programming tools for interacting with
intuitive physics environments. The framework employs a physics simulation in a
probabilistic way to infer about moves performed by an agent in a setting
governed by Newtonian laws of motion. However, methods of probabilistic
programs can be slow in such setting due to their need to generate many
samples. We complement the model with a model-free approach to aid the sampling
procedures in becoming more efficient through learning from experience during
game playing. We present an approach where combining model-free approaches (a
convolutional neural network in our model) and model-based approaches
(probabilistic physics simulation) is able to achieve what neither could alone.
This way the model outperforms an all model-free or all model-based approach.
We discuss a case study showing empirical results of the performance of the
model on the game of Flappy Bird.
