  Deep reinforcement learning has been successfully applied to several
visual-input tasks using model-free methods. In this paper, we propose a
model-based approach that combines learning a DNN-based transition model with
Monte Carlo tree search to solve a block-placing task in Minecraft. Our learned
transition model predicts the next frame and the rewards one step ahead given
the last four frames of the agent's first-person-view image and the current
action. Then a Monte Carlo tree search algorithm uses this model to plan the
best sequence of actions for the agent to perform. On the proposed task in
Minecraft, our model-based approach reaches the performance comparable to the
Deep Q-Network's, but learns faster and, thus, is more training sample
efficient.
