  Reasoning about the behaviour of physical objects is a key capability of
agents operating in physical worlds. Humans are very experienced in physical
reasoning while it remains a major challenge for AI. To facilitate research
addressing this problem, several benchmarks have been proposed recently.
However, these benchmarks do not enable us to measure an agent's granular
physical reasoning capabilities when solving a complex reasoning task. In this
paper, we propose a new benchmark for physical reasoning that allows us to test
individual physical reasoning capabilities. Inspired by how humans acquire
these capabilities, we propose a general hierarchy of physical reasoning
capabilities with increasing complexity. Our benchmark tests capabilities
according to this hierarchy through generated physical reasoning tasks in the
video game Angry Birds. This benchmark enables us to conduct a comprehensive
agent evaluation by measuring the agent's granular physical reasoning
capabilities. We conduct an evaluation with human players, learning agents, and
heuristic agents and determine their capabilities. Our evaluation shows that
learning agents, with good local generalization ability, still struggle to
learn the underlying physical reasoning capabilities and perform worse than
current state-of-the-art heuristic agents and humans. We believe that this
benchmark will encourage researchers to develop intelligent agents with
advanced, human-like physical reasoning capabilities. URL:
https://github.com/Cheng-Xue/Hi-Phy
