  As an emerging paradigm of content creation, AI-Generated Content (AIGC) has
been widely adopted by a large number of edge end users. However, the requests
for generated content from AIGC users have obvious diversity, and there remains
a notable lack of research addressing the variance in user demands for AIGC
services. This gap underscores a critical need for suitable AIGC service
selection mechanisms satisfying various AIGC user requirements under
resource-constrained edge environments. To address this challenge, this paper
proposes a novel Attention-based Diffusion Soft Actor-Critic (ADSAC) algorithm
to select the appropriate AIGC model in response to heterogeneous AIGC user
requests. Specifically, the ADSAC algorithm integrates a diffusion model as the
policy network in the off-policy reinforcement learning (RL) framework, to
capture the intricate relationships between the characteristics of AIGC tasks
and the integrated edge network states. Furthermore, an attention mechanism is
utilized to harness the contextual long-range dependencies present in state
feature vectors, enhancing the decision-making process. Extensive experiments
validate the effectiveness of our algorithm in enhancing the overall user
utility and reducing the crash rate of servers. Compared to the existing
methods, the proposed ADSAC algorithm outperforms existing methods, reducing
the overall user utility loss and the server crash rate by at least 58.3% and
58.4%, respectively. These results demonstrate our ADSAC algorithm is a robust
solution to the challenges of diverse and dynamic user requirements in
edge-based AIGC application environments.
