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.

翻译：最近的调查结果表明,人类利用物理模拟引擎的认知机制模拟物体的物理学。我们提议一个机器人框架,以部署概率性编程工具与直观物理环境进行互动。这个框架采用物理模拟,以概率性的方式推断代理人在牛顿运动法所规范的环境中的动作。然而,概率性程序方法在这种环境中可能缓慢,因为它们需要生成许多样本。我们用一种无型方法来补充模型,帮助取样程序通过在游戏中学习经验而提高效率。我们提出了一个方法,即将无型方法(我们模型中的共生神经网络)和基于模型的方法(概率性物理模拟)结合起来,既能够实现两者都无法单独实现的目标。这个模型比所有无型或所有模型方法都慢。我们讨论一个案例研究,显示Flappy Bird游戏模型的实绩经验。