Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and
  Option Portfolios   The QLBS model is a discrete-time option hedging and pricing model that is
based on Dynamic Programming (DP) and Reinforcement Learning (RL). It combines
the famous Q-Learning method for RL with the Black-Scholes (-Merton) model's
idea of reducing the problem of option pricing and hedging to the problem of
optimal rebalancing of a dynamic replicating portfolio for the option, which is
made of a stock and cash. Here we expand on several NuQLear (Numerical
Q-Learning) topics with the QLBS model. First, we investigate the performance
of Fitted Q Iteration for a RL (data-driven) solution to the model, and
benchmark it versus a DP (model-based) solution, as well as versus the BSM
model. Second, we develop an Inverse Reinforcement Learning (IRL) setting for
the model, where we only observe prices and actions (re-hedges) taken by a
trader, but not rewards. Third, we outline how the QLBS model can be used for
pricing portfolios of options, rather than a single option in isolation, thus
providing its own, data-driven and model independent solution to the (in)famous
volatility smile problem of the Black-Scholes model.

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The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph