Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A study of first-passage time minimization via Q-learning in heated
  gridworlds   Optimization of first-passage times is required in applications ranging from
nanobots navigation to market trading. In such settings, one often encounters
unevenly distributed noise levels across the environment. We extensively study
how a learning agent fares in 1- and 2- dimensional heated gridworlds with an
uneven temperature distribution. The results show certain bias effects in
agents trained via simple tabular Q-learning, SARSA, Expected SARSA and Double
Q-learning. While high learning rate prevents exploration of regions with
higher temperature, low enough rate increases the presence of agents in such
regions. The discovered peculiarities and biases of temporal-difference-based
reinforcement learning methods should be taken into account in real-world
physical applications and agent design.

26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin