# Reinforcement Learning for Option Hedging: Static Implied-Volatility Fit versus Shortfall-Aware Performance

arXiv: https://arxiv.org/abs/2601.01709 · HTML: https://mlsanity.com/paper/2601.01709/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds](https://mlsanity.com/paper/1712.04609/) (2017)
- [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, 2 of 2)

- [Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite](https://mlsanity.com/paper/2606.11257/) (2026)
- [Detecting Abnormal User Feedback Patterns through Temporal Sentiment Aggregation](https://mlsanity.com/paper/2604.00020/) (2026)

## Abstract (arXiv)

We extend the Q-learner in Black-Scholes (QLBS) framework by incorporating risk aversion and trading costs, and propose a novel Replication Learning of Option Pricing (RLOP) approach. Both methods are fully compatible with standard reinforcement learning algorithms and operate under market frictions. Using SPY and XOP option data, we evaluate performance along static and dynamic dimensions. Adaptive-QLBS achieves higher static pricing accuracy in implied volatility space, while RLOP delivers superior dynamic hedging performance by reducing shortfall probability. These results highlight the importance of evaluating option pricing models beyond static fit, emphasizing realized hedging outcomes.