Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning with Model Learning and Monte Carlo Tree
  Search in Minecraft   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.

37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin