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.

该论文提出了一个使用概率编程工具与物理模拟引擎相结合的框架，用于向机器人提供直观的物理环境交互能力，并通过模型自由方法使采样程序变得更高效，从而在模型自由方法和模型为基础的方法之间找到平衡，并通过模拟 Flappy Bird 游戏的实验结果，展示了该模型的绩效。