Gradient Optimization for Single-State RMDPs

TL;DR

This paper investigates gradient-based optimization methods for single-state randomized Markov decision processes (RMDPs), aiming to improve decision-making reliability in high-stakes, data-driven environments like autonomous systems.

Contribution

It introduces novel gradient optimization techniques tailored for single-state RMDPs, providing theoretical insights into their convergence and stability properties.

Findings

Proposes a new gradient optimization algorithm for single-state RMDPs

Demonstrates improved stability over existing methods

Provides theoretical analysis of convergence behavior

Abstract

As modern problems such as autonomous driving, control of robotic components, and medical diagnostics have become increasingly difficult to solve analytically, data-driven decision-making has seen a large gain in interest. Where there are problems with more dimensions of complexity than can be understood by people, data-driven solutions are a strong option. Many of these methods belong to a subdivision of machine learning known as reinforcement learning. Unfortunately, data-driven models often come with uncertainty in how they will perform in the worst of scenarios. Since the solutions are not derived analytically many times, these models will fail unpredictably. In fields such as autonomous driving and medicine, the consequences of these failures could be catastrophic. Various methods are being explored to resolve this issue and one of them is known as adversarial learning. It pits two models against each other by having one model optimize its goals as the opposite of the other model's goals. This type of training has the potential to find models which perform reliably in complex and high stakes settings, although it is not certain when this type of training will work. The goal is to gain insight about when these types of models will reach stable solutions.