Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transferring Deep Reinforcement Learning with Adversarial Objective and
  Augmentation   In the past few years, deep reinforcement learning has been proven to solve
problems which have complex states like video games or board games. The next
step of intelligent agents would be able to generalize between tasks, and using
prior experience to pick up new skills more quickly. However, most
reinforcement learning algorithms for now are often suffering from catastrophic
forgetting even when facing a very similar target task. Our approach enables
the agents to generalize knowledge from a single source task, and boost the
learning progress with a semisupervised learning method when facing a new task.
We evaluate this approach on Atari games, which is a popular reinforcement
learning benchmark, and show that it outperforms common baselines based on
pre-training and fine-tuning.

33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio