Simon M. Lucas,Alexander Dockhorn,Vanessa Volz,Chris Bamford,Raluca D. Gaina,Ivan Bravi,Diego Perez-Liebana,Sanaz Mostaghim,Rudolf Kruse
Abstract URL: http://arxiv.org/abs/1903.12508v1
This paper investigates the effect of learning a forward model on the
performance of a statistical forward planning agent. We transform Conway's Game
of Life simulation into a single-player game where the objective can be either
to preserve as much life as possible or to extinguish all life as quickly as
In order to learn the forward model of the game, we formulate the problem in
a novel way that learns the local cell transition function by creating a set of
supervised training data and predicting the next state of each cell in the grid
based on its current state and immediate neighbours. Using this method we are
able to harvest sufficient data to learn perfect forward models by observing
only a few complete state transitions, using either a look-up table, a decision
tree or a neural network.
In contrast, learning the complete state transition function is a much harder
task and our initial efforts to do this using deep convolutional auto-encoders
were less successful.
We also investigate the effects of imperfect learned models on prediction
errors and game-playing performance, and show that even models with significant
errors can provide good performance.