  We demonstrate the use of conditional autoregressive generative models (van
den Oord et al., 2016a) over a discrete latent space (van den Oord et al.,
2017b) for forward planning with MCTS. In order to test this method, we
introduce a new environment featuring varying difficulty levels, along with
moving goals and obstacles. The combination of high-quality frame generation
and classical planning approaches nearly matches true environment performance
for our task, demonstrating the usefulness of this method for model-based
planning in dynamic environments.
