Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Relational Abstractions for Generalized Reinforcement Learning on
  Symbolic Problems   Reinforcement learning in problems with symbolic state spaces is challenging
due to the need for reasoning over long horizons. This paper presents a new
approach that utilizes relational abstractions in conjunction with deep
learning to learn a generalizable Q-function for such problems. The learned
Q-function can be efficiently transferred to related problems that have
different object names and object quantities, and thus, entirely different
state spaces. We show that the learned generalized Q-function can be utilized
for zero-shot transfer to related problems without an explicit, hand-coded
curriculum. Empirical evaluations on a range of problems show that our method
facilitates efficient zero-shot transfer of learned knowledge to much larger
problem instances containing many objects.

20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin