Work

Uncertainty Quantification of the Hypervolume for Evolutionary Multi-Objective Reinforcement Learning

Year: 2025

Type: conference-paper

Abstract: Many real-world problems involve optimizing multiple conflicting objectives simultaneously, often by a series of decisions. Multi-objective reinforcement learning addresses these challenges by training agents to learn policies that balance multiple reward signals from the environment.

Source: Genetic and Evolutionary Computation Conference Companion (GECCO Companion)

Authors: Alberto Maximiliano Millán, Carlos Ignacio Hernández Castellanos

Institution: Universidad Nacional Autónoma de México

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4413209554

FWCI:

Cites: 6

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Related to: 10