# RLOP: RL Methods in Option Pricing from a Mathematical Perspective

arXiv: https://arxiv.org/abs/2205.05600 · HTML: https://mlsanity.com/paper/2205.05600/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Policy Gradient for Coherent Risk Measures](https://mlsanity.com/paper/1502.03919/) (2015)
- [QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds](https://mlsanity.com/paper/1712.04609/) (2017)
- [Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs (SciPhy RL for DOCTR-L)](https://mlsanity.com/paper/2104.01040/) (2021)
- [Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures](https://mlsanity.com/paper/2109.04001/) (2021)

## Abstract (arXiv)

Abstract In this work, we build two environments, namely the modified QLBS and RLOP models, from a mathematics perspective which enables RL methods in option pricing through replicating by portfolio. We implement the environment specifications (the source code can be found at https://github.com/owen8877/RLOP), the learning algorithm, and agent parametrization by a neural network. The learned optimal hedging strategy is compared against the BS prediction. The effect of various factors is considered and studied based on how they affect the optimal price and position.