mlsanityData to 14 Oct 2026

Distributional Reinforcement Learning on Path-dependent Options

arXiv 2507.12657 ↗First posted 16 Jul 2025q-fin.MF cs.LGcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

We reinterpret and propose a framework for pricing path-dependent financial derivatives by estimating the full distribution of payoffs using Distributional Reinforcement Learning (DistRL). Unlike traditional methods that focus on expected option value, our approach models the entire conditional distribution of payoffs, allowing for risk-aware pricing, tail-risk estimation, and enhanced uncertainty quantification. We demonstrate the efficacy of this method on Asian options, using quantile-based value function approximators.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 21 references that are in the map. First posted on arXiv 16 Jul 2025.

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8 of 21 references.

References · 8

  1. 2017A Distributional Perspective on Reinforcement Learningcited by 765
  2. 2017Solving high-dimensional partial differential equations using deep learningcited by 556
  3. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 474
  4. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 369
  5. 2018Deep Hedgingcited by 108
  6. 2018An Analysis of Categorical Distributional Reinforcement Learningcited by 68
  7. 2021Deep Hedging of Derivatives Using Reinforcement Learningcited by 30
  8. 2022Gamma and Vega Hedging Using Deep Distributional Reinforcement Learningcited by 15

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  1. 2025Risk-averse policies for natural gas futures trading using distributional reinforcement learning
  2. 2024EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
  3. 2022Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective
  4. 2025Value Flows
  5. 2025Optimizing Return Distributions with Distributional Dynamic Programming
  6. 2026Autonomous AI Agents for Option Hedging: Enhancing Financial Stability through Shortfall Aware Reinforcement Learning
  7. 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Survey
  8. 2026Reinforcement Learning for Option Hedging: Static Implied-Volatility Fit versus Shortfall-Aware Performance