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Adaptación eficiente de políticas multiagente con reutilización bayesiana de políticas y conciencia contrastiva
1College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China.
Resumen
La Reutilización Bayesiana de Políticas con Conciencia Contrastiva Invariante a la Vista (BPR-VCA) mejora el aprendizaje multiagente en entornos parcialmente observables. Este marco de IA mejora la adaptación de políticas y el reconocimiento del oponente para un mejor rendimiento en escenarios competitivos.
Área de la Ciencia:
- Inteligencia Artificial
- Sistemas Multiagente
- Aprendizaje por Refuerzo
Sus antecedentes:
- La Reutilización Bayesiana de Políticas (BPR) permite la adaptación a oponentes no estacionarios, pero se limita a entornos de un solo agente y totalmente observables.
- La observabilidad parcial y la coordinación multiagente presentan desafíos significativos para los marcos de adaptación de políticas existentes.
Objetivo del estudio:
- Proponer BPR-VCA, integrando BPR con aprendizaje contrastivo multivista para una adaptación eficiente de políticas en entornos multiagente parcialmente observables.
- Permitir a los agentes aprender de observaciones locales y lograr un consenso sobre la dinámica global a pesar de la información limitada.
Principales métodos:
- BPR-VCA utiliza trayectorias de observación locales para actualizaciones de creencias, abordando la observabilidad parcial.
- Un módulo de Conciencia Contrastiva Invariante a la Vista (VCA) fomenta el consenso sobre los cambios ambientales integrando vistas globales y locales.
- La actualización descentralizada de creencias en línea permite a los agentes seleccionar políticas conjuntas apropiadas.
Principales resultados:
- BPR-VCA demostró un rendimiento superior en cuatro escenarios competitivos.
- El marco logró recompensas episódicas y acumuladas más altas en comparación con las líneas de base de última generación.
- Se observó un reconocimiento de oponentes más rápido y preciso, junto con tasas de victoria más altas.
Conclusiones:
- BPR-VCA aborda eficazmente los desafíos de observabilidad parcial y coordinación multiagente en la adaptación de políticas.
- El método propuesto ofrece una solución robusta para aplicaciones del mundo real que requieren agentes de IA adaptativos en entornos dinámicos.
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