Uncertainty Quantification of the Hypervolume for Evolutionary Multi-Objective Reinforcement Learning
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.
Authors
- Carlos Hernández (ORCID: https://orcid.org/0000-0003-4303-6866)
- Alberto Maximiliano Millán
Institutions
- Universidad Internacional de América (MX)
Publication Details
- Journal
- Proceedings of the Genetic and Evolutionary Computation Conference Companion
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1145/3712255.3726775
- Primary Topic
- Advanced Multi-Objective Optimization Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00