PHYRE: A New Benchmark for Physical Reasoning

Abstract

Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. We test several modern learning algorithms on PHYRE and find that these algorithms fall short in solving the puzzles efficiently. We expect that PHYRE will encourage the development of novel sample-efficient agents that learn efficient but useful models of physics. For code and to play PHYRE for yourself, please visit https://player.phyre.ai.

References in corpus (13)

• A Contextual-Bandit Approach to Personalized News Article Recommendation
• SGDR: Stochastic Gradient Descent with Warm Restarts
• Building Generalizable Agents with a Realistic and Rich 3D Environment
• Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
• The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
• Learning Physical Intuition of Block Towers by Example
• To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction
• Reasoning About Physical Interactions with Object-Oriented Prediction and Planning
• A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding
• IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning
• Probing Physics Knowledge Using Tools from Developmental Psychology
• Prediction Under Uncertainty with Error-Encoding Networks
• Generalizable Features From Unsupervised Learning