Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal Dispatch in Emergency Service System via Reinforcement Learning   In the United States, medical responses by fire departments over the last
four decades increased by 367%. This had made it critical to decision makers in
emergency response departments that existing resources are efficiently used. In
this paper, we model the ambulance dispatch problem as an average-cost Markov
decision process and present a policy iteration approach to find an optimal
dispatch policy. We then propose an alternative formulation using post-decision
states that is shown to be mathematically equivalent to the original model, but
with a much smaller state space. We present a temporal difference learning
approach to the dispatch problem based on the post-decision states. In our
numerical experiments, we show that our obtained temporal-difference policy
outperforms the benchmark myopic policy. Our findings suggest that emergency
response departments can improve their performance with minimal to no cost.

16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga