AI systems need exact descriptions of tasks to be performed.

Deep learning sometimes appears to work in unexpected ways.

Current test-time adaptation (TT A) approaches aim to adapt a machine learning model to environments that change continuously.

Specifically, we integrate two essential sub-tasks, i.e., traffic data imputation and decision-making, by leveraging a Partial Rewards Conditioned Diffusion (PRCD) model to prevent missing rewards

Code is at this URL.