Learning Shared Dynamics with Meta-World Models
  Humans have consciousness as the ability to perceive events and objects: a
mental model of the world developed from the most impoverished of visual
stimuli, enabling humans to make rapid decisions and take actions. Although
spatial and temporal aspects of different scenes are generally diverse, the
underlying physics among environments still work the same way, thus learning an
abstract description of shared physical dynamics helps human to understand the
world. In this paper, we explore building this mental world with neural network
models through multi-task learning, namely the meta-world model. We show
through extensive experiments that our proposed meta-world models successfully
capture the common dynamics over the compact representations of visually
different environments from Atari Games. We also demonstrate that agents
equipped with our meta-world model possess the ability of visual
self-recognition, i.e., recognize themselves from the reflected mirrored
environment derived from the classic mirror self-recognition test (MSR).
