However, directly applying existing subgame solving techniques may be difficult, due to the intricate nature and substantial size of many real-world games.

InComputerVision,however,almost all performant networks are "dense", that is, every input is processed by every parameter. We present a Vision MoE (V-MoE), a sparse version of the Vision Transformer, that is scalable and competitive with the largest dense networks.

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

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.