# Applying Reinforcement Learning to Option Pricing and Hedging

arXiv: https://arxiv.org/abs/2310.04336 · HTML: https://mlsanity.com/paper/2310.04336/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Deep Hedging of Derivatives Using Reinforcement Learning](https://mlsanity.com/paper/2103.16409/) (2021)
- [QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds](https://mlsanity.com/paper/1712.04609/) (2017)
- [Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning](https://mlsanity.com/paper/2205.05614/) (2022)
- [Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions](https://mlsanity.com/paper/2207.07467/) (2022)
- [The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios](https://mlsanity.com/paper/1801.06077/) (2018)

## Built on by (most cited first, 1 of 1)

- [Risk-Averse Reinforcement Learning with Itakura-Saito Loss](https://mlsanity.com/paper/2505.16925/) (2025)

## Abstract (arXiv)

This thesis provides an overview of the recent advances in reinforcement learning in pricing and hedging financial instruments, with a primary focus on a detailed explanation of the Q-Learning Black Scholes approach, introduced by Halperin (2017). This reinforcement learning approach bridges the traditional Black and Scholes (1973) model with novel artificial intelligence algorithms, enabling option pricing and hedging in a completely model-free and data-driven way. This paper also explores the algorithm's performance under different state variables and scenarios for a European put option. The results reveal that the model is an accurate estimator under different levels of volatility and hedging frequency. Moreover, this method exhibits robust performance across various levels of option's moneyness. Lastly, the algorithm incorporates proportional transaction costs, indicating diverse impacts on profit and loss, affected by different statistical properties of the state variables.