Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extending Deep Reinforcement Learning Frameworks in Cryptocurrency
  Market Making   There has been a recent surge in interest in the application of artificial
intelligence to automated trading. Reinforcement learning has been applied to
single- and multi-instrument use cases, such as market making or portfolio
management. This paper proposes a new approach to framing cryptocurrency market
making as a reinforcement learning challenge by introducing an event-based
environment wherein an event is defined as a change in price greater or less
than a given threshold, as opposed to by tick or time-based events (e.g., every
minute, hour, day, etc.). Two policy-based agents are trained to learn a market
making trading strategy using eight days of training data and evaluate their
performance using 30 days of testing data. Limit order book data recorded from
Bitmex exchange is used to validate this approach, which demonstrates improved
profit and stability compared to a time-based approach for both agents when
using a simple multi-layer perceptron neural network for function approximation
and seven different reward functions.

7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom