直觉物理游戏中的概率编程机器人
Probabilistic Programming Bots in Intuitive Physics Game Play
摘要：
最近的研究结果表明，人类部署物理模拟引擎的认知机制来模拟对象的物理特性。我们提出了一个框架，机器人部署概率编程工具与直观的物理环境进行交互。该框架以概率的方式采用物理模拟来推断代理在牛顿运动定律支配的设置中执行的移动。然而，概率程序的方法在这样的设置中可能很慢，因为它们需要生成许多样本。我们补充模型与无模型的方法，以帮助采样程序变得更有效，通过学习经验，在玩游戏。我们提出了一种方法，将无模型方法（我们模型中的卷积神经网络）和基于模型的方法（概率物理模拟）相结合，能够实现两者都不能单独实现的目标。通过这种方式，模型优于所有无模型或所有基于模型的方法。我们讨论了一个案例研究，展示了Flappy Bird游戏模型性能的实证结果。
英文摘要：
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.
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