  We investigate opportunities and challenges for improving unsupervised
machine learning using four common strategies with a long history in physics:
divide-and-conquer, Occam's razor, unification and lifelong learning. Instead
of using one model to learn everything, we propose a novel paradigm centered
around the learning and manipulation of *theories*, which parsimoniously
predict both aspects of the future (from past observations) and the domain in
which these predictions are accurate. Specifically, we propose a novel
generalized-mean-loss to encourage each theory to specialize in its
comparatively advantageous domain, and a differentiable description length
objective to downweight bad data and "snap" learned theories into simple
symbolic formulas. Theories are stored in a "theory hub", which continuously
unifies learned theories and can propose theories when encountering new
environments. We test our implementation, the toy "AI Physicist" learning
agent, on a suite of increasingly complex physics environments. From
unsupervised observation of trajectories through worlds involving random
combinations of gravity, electromagnetism, harmonic motion and elastic bounces,
our agent typically learns faster and produces mean-squared prediction errors
about a billion times smaller than a standard feedforward neural net of
comparable complexity, typically recovering integer and rational theory
parameters exactly. Our agent successfully identifies domains with different
laws of motion also for a nonlinear chaotic double pendulum in a piecewise
constant force field.
