Diffusion-Guided Multi-Objective Reinforcement Learning for Hybrid Action Optimization in Collaborative Edge AIGC Networks

Adaptive AI Agent Migration via Generative Diffusion-based Reinforcement Learning in Edge Intelligence Systems

This work introduces a diffusion model as a generative prior to produce high-quality global deployment plans, effectively avoiding the local-optima problem common in conventional reinforcement learning.

Solving the hybrid action space problem in edge computing: a graph-aware reinforcement learning approach for container management

A-MADiff: Attention-Guided Multi-Agent DRL with Diffusion Policies for Memory-Aware Task Orchestration in Mobile AIGC Networks

A cooperative multi-agent orchestration framework, in which each edge node is equipped with a scheduling agent to route tasks to local ASPs or neighboring edge nodes, and an attention-guided centralized critic to estimate per-agent values from cross-agent states under GPU memory heterogeneity is proposed.

A Reinforcement Learning-Driven Multi-Agent Cooperative Grey Wolf Algorithm for Influence Maximization

Influence maximization (IM) in social networks aims to identify the optimal set of seed nodes that maximizes influence spread under a given diffusion model. The standard Grey Wolf Optimizer (GWO) suffers from two fundamental limitations when applied to this problem: an inflexible exploration–exploitation transition con...

AGTA: Topology-Aware Sequential Decision-Making in Multi-Agent Reinforcement Learning

Action Generation with Topology Awareness (AGTA), a topology-aware sequential decision-making framework in MARL that integrates inter-agent correlation modeling with topology-guided decision-order optimization, and outperforms the state-of-the-art counterparts.

A hierarchical reinforcement learning based subtask-coordinated scheduling method for constrained multi-objective evolutionary algorithm

In recent years, constrained multi-objective optimization problems(CMOPs) remain challenging due to the complex structure of feasible regions, the difficulty of balancing convergence and diversity, and the lack of adaptive operator scheduling mechanisms. To address these issues, this paper proposes a hierarchical reinf...