mlsanityData to 14 Oct 2026

Adversarial Reinforcement Learning: A Duality-Based Approach To Solving Optimal Control Problems

arXiv 2506.00801 ↗First posted 1 Jun 2025math.OCcited by 1

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

Abstract (arXiv)

We propose an adversarial deep reinforcement learning (ADRL) algorithm for high-dimensional stochastic control problems. Inspired by the information relaxation duality, ADRL reformulates the control problem as a min-max optimization between policies and adversarial penalties, enforcing non-anticipativity while preserving optimality. Numerical experiments demonstrate ADRL's superior performance to yield tight dual gaps. Our results highlight the potential of ADRL as a robust computational framework for high-dimensional stochastic control in simulation-based optimization contexts.

Key facts. Cited by 1 paper 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 8 of its 31 references that are in the map. First posted on arXiv 1 Jun 2025.

Builds on

8 of 31 references.

References · 8

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Solving high-dimensional partial differential equations using deep learningcited by 556
  3. 2017Adversarial Attacks on Neural Network Policiescited by 320
  4. 2018Deep Hedgingcited by 108
  5. 2016Deep Learning Approximation for Stochastic Control Problemscited by 55
  6. 2018Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysiscited by 24
  7. 2018Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applicationscited by 16
  8. 2020Deep Empirical Risk Minimization in finance: looking into the futurecited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Dual Approaches to Stochastic Control via SPDEs and the Pathwise Hopf Formulacited by 0

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  1. 2026Robust Adversarial Policy Optimization Under Dynamics Uncertainty
  2. 2025A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning
  3. 2024An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
  4. 2026Robust Policy Optimization via Adversarial Importance Sampling
  5. 2024Adversarial Constrained Policy Optimization: Improving Constrained Reinforcement Learning by Adapting Budgets
  6. 2024Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
  7. 2025Towards Optimal Adversarial Robust Reinforcement Learning with Infinity Measurement Error
  8. 2026TCRL: Temporal-Coupled Adversarial Training for Robust Constrained Reinforcement Learning in Worst-Case Scenarios