This is useful for a variety of applications like sports analysis (e.g.

Our first step is to prove the parameterization (Eq. 3) provides local attention after the Note that the weight and bias terms in theaboveformulation (Eq. Assume the position-based function at each head is learned to perform'hard attention' on one of its surrounding positions,i.e., an extreme semi-dynamic attention. To demonstrate this phenomenon, we plot and compare the impacts ofΦc and Φp6 on Φa in the middle and right of Fig. S4 and visualize learned position-based attentionΦp of iRPE in Fig. S5. As seen from Tab. S17, there exist noticeable performance gaps between the models (b, f, g, h) (withoutΦp)and(a,d,e,i)(withΦp). Without adaptiveattention (model (c)),Φp imposes stronger locality onevery layer.

Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments.

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