DeepAI AI Chat
Log In Sign Up

Probabilistic Programming Bots in Intuitive Physics Game Play

by   Fahad Alhasoun, et al.

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.


page 2

page 3

page 4

page 6

page 7

page 8


Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Integrating model-free and model-based approaches in reinforcement learn...

Neural Networks Predict Fluid Dynamics Solutions from Tiny Datasets

In computational fluid dynamics, it often takes days or weeks to simulat...

Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization

Perimeter control maintains high traffic efficiency within protected reg...

Combining Q-Learning and Search with Amortized Value Estimates

We introduce "Search with Amortized Value Estimates" (SAVE), an approach...

Model-free, Model-based, and General Intelligence

During the 60s and 70s, AI researchers explored intuitions about intelli...

Learning Visible Connectivity Dynamics for Cloth Smoothing

Robotic manipulation of cloth remains challenging for robotics due to th...