  We present ongoing work on a tool that consists of two parts: (i) A raw
micro-level abstract world simulator with an interface to (ii) a 3D game
engine, translator of raw abstract simulator data to photorealistic graphics.
Part (i) implements a dedicated cellular automata (CA) on reconfigurable
hardware (FPGA) and part (ii) interfaces with a deep learning framework for
training neural networks. The bottleneck of such an architecture usually lies
in the fact that transferring the state of the whole CA significantly slows
down the simulation. We bypass this by sending only a small subset of the
general state, which we call a 'locus of visibility', akin to a torchlight in a
darkened 3D space, into the simulation. The torchlight concept exists in many
games but these games generally only simulate what is in or near the locus. Our
chosen architecture will enable us to simulate on a micro level outside the
locus. This will give us the advantage of being able to create a larger and
more fine-grained simulation which can be used to train neural networks for use
in games.
