Heuristic search aims to obtain a least-cost path to the destination. Onthewaytothedestination, heuristic search is achieved by progressively selecting the extended path. As the other part, the cost of estimating the distance from noden to the destinationh(n) could be similar to the domain-specific representationsH(x). The fundament representationsF(x)could be calculated by the sum ofG(x)andH(x). Meanwhile, since bothGandF could classify source samples correctly,the differences on source domain are little, which means S(G) = S(F).

We first raise and tackle a "time synchronization" issue between the agent and the environment in non-stationary reinforcement learning (RL), a crucial factor

Big data has enabled deep learning algorithms achieve rapid advancements. In particular, stateof-the-art generative adversarial networks (GANs) [11] are able to generate high-fidelity natural images of diverse categories [2,18]. Many computer vision and graphics applications have been enabled[32,43,53].

To tackle this, test-time adaptation (TTA) [44] isproposed toboost model performance atinference time. The proposed objective in Eq.(10) requires the joint training of task modelfθ(x) and diffusion model φ(xt,t,cy) over all conditions{cy : y Y} simutaneously.