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.
@article{arxiv.2506.00801,
title = {Adversarial Reinforcement Learning: A Duality-Based Approach To Solving Optimal Control Problems},
author = {Nan Chen and Mengzhou Liu and Xiaoyan Wang and Nanyi Zhang},
journal= {arXiv preprint arXiv:2506.00801},
year = {2025}
}