  As the next generation of mobile systems evolves, artificial intelligence
(AI) is expected to deeply integrate with wireless communications for resource
management in variable environments. In particular, deep reinforcement learning
(DRL) is an important tool for addressing stochastic optimization issues of
resource allocation. However, DRL has to start each new training process from
the beginning once the state and action spaces change, causing low sample
efficiency and poor generalization ability. Moreover, each DRL training process
may take a large number of epochs to converge, which is unacceptable for
time-sensitive scenarios. In this paper, we adopt an alternative AI technology,
namely, the Decision Transformer (DT), and propose a DT-based adaptive decision
architecture for wireless resource management. This architecture innovates
through constructing pre-trained models in the cloud and then fine-tuning
personalized models at the edges. By leveraging the power of DT models learned
over extensive datasets, the proposed architecture is expected to achieve rapid
convergence with many fewer training epochs and higher performance in a new
context, e.g., similar tasks with different state and action spaces, compared
with DRL. We then design DT frameworks for two typical communication scenarios:
Intelligent reflecting surfaces-aided communications and unmanned aerial
vehicle-aided edge computing. Simulations demonstrate that the proposed DT
frameworks achieve over $3$-$6$ times speedup in convergence and better
performance relative to the classic DRL method, namely, proximal policy
optimization.
