Natural actor-critic for robust reinforcement learning with function approximation
We study robust reinforcement learning (RL) with the goal of determining a well-performing
policy that is robust against model mismatch between the training simulator and the testing …
policy that is robust against model mismatch between the training simulator and the testing …
Double pessimism is provably efficient for distributionally robust offline reinforcement learning: Generic algorithm and robust partial coverage
We study distributionally robust offline reinforcement learning (RL), which seeks to find an
optimal robust policy purely from an offline dataset that can perform well in perturbed …
optimal robust policy purely from an offline dataset that can perform well in perturbed …
Single-trajectory distributionally robust reinforcement learning
As a framework for sequential decision-making, Reinforcement Learning (RL) has been
regarded as an essential component leading to Artificial General Intelligence (AGI) …
regarded as an essential component leading to Artificial General Intelligence (AGI) …
Model-free robust average-reward reinforcement learning
Abstract Robust Markov decision processes (MDPs) address the challenge of model
uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs. In …
uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs. In …
Decentralized robust v-learning for solving markov games with model uncertainty
The Markov game is a popular reinforcement learning framework for modeling competitive
players in a dynamic environment. However, most of the existing works on Markov games …
players in a dynamic environment. However, most of the existing works on Markov games …
Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems
In this paper we develop a Stochastic Gradient Langevin Dynamics (SGLD) algorithm
tailored for solving a certain class of non-convex distributionally robust optimisation …
tailored for solving a certain class of non-convex distributionally robust optimisation …
Sequential Decision-Making under Uncertainty: A Robust MDPs review
W Ou, S Bi - arXiv preprint arXiv:2404.00940, 2024 - arxiv.org
This review paper provides an in-depth overview of the evolution and advancements in
Robust Markov Decision Processes (RMDPs), a field of paramount importance for its role in …
Robust Markov Decision Processes (RMDPs), a field of paramount importance for its role in …
Robust option pricing with volatility term structure--An empirical study for variance options
AMG Cox, AM Grass - arXiv preprint arXiv:2312.09201, 2023 - arxiv.org
The robust option pricing problem is to find upper and lower bounds on fair prices of
financial claims using only the most minimal assumptions. It contrasts with the classical …
financial claims using only the most minimal assumptions. It contrasts with the classical …
On Practical Robust Reinforcement Learning: Adjacent Uncertainty Set and Double-Agent Algorithm
U Hwang, S Hong - IEEE Transactions on Neural Networks and …, 2024 - ieeexplore.ieee.org
Robust reinforcement learning (RRL) aims to seek a robust policy by optimizing the worst
case performance over an uncertainty set. This set contains some perturbed Markov …
case performance over an uncertainty set. This set contains some perturbed Markov …
Regularized Q-learning through Robust Averaging
P Schmitt-Förster, T Sutter - arXiv preprint arXiv:2405.02201, 2024 - arxiv.org
We propose a new Q-learning variant, called 2RA Q-learning, that addresses some
weaknesses of existing Q-learning methods in a principled manner. One such weakness is …
weaknesses of existing Q-learning methods in a principled manner. One such weakness is …