Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Reinforcement Learning Environment for Mathematical Reasoning via
  Program Synthesis   We convert the DeepMind Mathematics Dataset into a reinforcement learning
environment by interpreting it as a program synthesis problem. Each action
taken in the environment adds an operator or an input into a discrete compute
graph. Graphs which compute correct answers yield positive reward, enabling the
optimization of a policy to construct compute graphs conditioned on problem
statements. Baseline models are trained using Double DQN on various subsets of
problem types, demonstrating the capability to learn to correctly construct
graphs despite the challenges of combinatorial explosion and noisy rewards.

5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex