arXiv:2512.04958v1 Announce Type: new 
Abstract: The main focus of Hierarchical Reinforcement Learning (HRL) is studying how large Markov Decision Processes (MDPs) can be more efficiently solved when addressed in a modular way, by combining partial solutions computed for smaller subtasks. Despite their very intuitive role for learning, most notions of MDP abstractions proposed in the HRL literature have limited expressive power or do not possess formal efficiency guarantees. This work addresses these fundamental issues by defining Realizable Abstractions, a new relation between generic low-level MDPs and their associated high-level decision processes. The notion we propose avoids non-Markovianity issues and has desirable near-optimality guarantees. Indeed, we show that any abstract policy for Realizable Abstractions can be translated into near-optimal policies for the low-level MDP, through a suitable composition of options. As demonstrated in the paper, these options can be expressed as solutions of specific constrained MDPs. Based on these findings, we propose RARL, a new HRL algorithm that returns compositional and near-optimal low-level policies, taking advantage of the Realizable Abstraction given in the input. We show that RARL is Probably Approximately Correct, it converges in a polynomial number of samples, and it is robust to inaccuracies in the abstraction.

تقدم دراسة حديثة مفاهيم جديدة تحت عنوان

Un estudio reciente presenta las Abstracciones Realizables en el Aprendizaje por Refuerzo Jerárquico (ARJ), abordando la eficiencia en la resolución de grandes Procesos de Decisión de Markov (PDM) mediante enfoques modulares. Esta nueva relación entre los PDM de bajo nivel y los procesos de decisión de alto nivel busca superar las limitaciones de las abstracciones de PDM existentes, proporcionando garantías de casi-optimalidad para las políticas abstractas.

Une étude récente présente les Abstractions Réalisables dans l'Apprentissage par Renforcement Hiérarchique (ARH), abordant l'efficacité de la résolution de grands Processus de Décision Markoviens (PDM) par des approches modulaires. Cette nouvelle relation entre les PDM de bas niveau et les processus décisionnels de haut niveau vise à surmonter les limitations des abstractions PDM existantes, offrant des garanties de quasi-optimalité pour les politiques abstraites.

A recent study introduces Realizable Abstractions in Hierarchical Reinforcement Learning (HRL), addressing the efficiency of solving large Markov Decision Processes (MDPs) through modular approaches. This new relation between low-level MDPs and high-level decision processes aims to overcome limitations in existing MDP abstractions, providing near-optimality guarantees for abstract policies.

Realizable Abstractions: Near-Optimal Hierarchical Reinforcement Learning

arXiv:2512.08052v1 Announce Type: cross 
Abstract: Embodied agents, such as robots and virtual characters, must continuously select actions to execute tasks effectively, solving complex sequential decision-making problems. Given the difficulty of designing such controllers manually, learning-based approaches have emerged as promising alternatives, most notably Deep Reinforcement Learning (DRL) and Deep Imitation Learning (DIL). DRL leverages reward signals to optimize behavior, while DIL uses expert demonstrations to guide learning. This document introduces DRL and DIL in the context of embodied agents, adopting a concise, depth-first approach to the literature. It is self-contained, presenting all necessary mathematical and machine learning concepts as they are needed. It is not intended as a survey of the field; rather, it focuses on a small set of foundational algorithms and techniques, prioritizing in-depth understanding over broad coverage. The material ranges from Markov Decision Processes to REINFORCE and Proximal Policy Optimization (PPO) for DRL, and from Behavioral Cloning to Dataset Aggregation (DAgger) and Generative Adversarial Imitation Learning (GAIL) for DIL.

تُبرز مقدمة التعلم العميق المعزز (DRL) والتعلم العميق بالاقتداء (DIL) أهمية الأساليب المعتمدة على التعلم للوكالات المجسدة، مثل الروبوتات والشخصيات الافتراضية، التي يجب أن تتنقل في مهام اتخاذ القرار المعقدة. يركز هذا المستند على الخوارزميات الأساسية مثل REINFORCE وتحسين السياسة القريبة، مقدماً نظرة عامة موجزة عن المفاهيم الأساسية في هذا المجال.

La introducción del Aprendizaje por Refuerzo Profundo (DRL) y del Aprendizaje por Imitación Profunda (DIL) resalta la importancia de los enfoques basados en el aprendizaje para agentes embebidos, como robots y personajes virtuales, que deben navegar en tareas complejas de toma de decisiones. Este documento enfatiza algoritmos fundamentales como REINFORCE y Optimización de Políticas Proximales, proporcionando una visión concisa de los conceptos esenciales en el campo.

L'introduction de l'apprentissage par renforcement profond (DRL) et de l'apprentissage par imitation profond (DIL) souligne l'importance des approches basées sur l'apprentissage pour les agents incarnés, tels que les robots et les personnages virtuels, qui doivent naviguer dans des tâches de prise de décision complexes. Ce document met l'accent sur des algorithmes fondamentaux comme REINFORCE et l'optimisation de politique proximale, fournissant un aperçu concis des concepts essentiels dans le domaine.

The introduction of Deep Reinforcement Learning (DRL) and Deep Imitation Learning (DIL) highlights the significance of learning-based approaches for embodied agents, such as robots and virtual characters, which must navigate complex decision-making tasks. This document emphasizes foundational algorithms like REINFORCE and Proximal Policy Optimization, providing a concise overview of essential concepts in the field.

Realizable Abstractions: Near-Optimal Hierarchical Reinforcement Learning

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