  Deep reinforcement learning has recently shown many impressive successes.
However, one major obstacle towards applying such methods to real-world
problems is their lack of data-efficiency. To this end, we propose the
Bottleneck Simulator: a model-based reinforcement learning method which
combines a learned, factorized transition model of the environment with rollout
simulations to learn an effective policy from few examples. The learned
transition model employs an abstract, discrete (bottleneck) state, which
increases sample efficiency by reducing the number of model parameters and by
exploiting structural properties of the environment. We provide a mathematical
analysis of the Bottleneck Simulator in terms of fixed points of the learned
policy, which reveals how performance is affected by four distinct sources of
error: an error related to the abstract space structure, an error related to
the transition model estimation variance, an error related to the transition
model estimation bias, and an error related to the transition model class bias.
Finally, we evaluate the Bottleneck Simulator on two natural language
processing tasks: a text adventure game and a real-world, complex dialogue
response selection task. On both tasks, the Bottleneck Simulator yields
excellent performance beating competing approaches.
