contextual knowledge including a description of the bbox prediction history and the related circumstantial multi-layer feature representations. This is compactly represented as the 64-dim vector $\vec{s}_t$.

**Action**: Fig. \[fig\_6\] enumerates the set of ten actions $\mathcal{A}$ organized into two categories: transformation actions and trigger actions, as considered in this work. At time $t$, our agent decides on a specific action $a_t \in \mathcal{A}$ drawn from a distribution function of Eq. , which lands on a subsequent bbox prediction $\vec{b}_{t+1}$: If it is a horizontal move, we have $b^x_{t+1} = b^x_{t} + d$ with $d \in \mathbb{R}$ being negative or positive, which corresponds to move leftward or rightward respectively; Similarly, for a vertical move, $b^y_{t+1} = b^y_{t} + d$ with $d<0$ or $d>0$ for moving upward or downward, respectively; If it is an aspect ratio change action, we have either $b^h_{t+1} = b^h_{t} + d$ for changing the height or $b^w_{t+1} = b^w_{t} + d$ for changing the width; A scale change action amounts to modify the bbox size, which is described by both $b^h_{t+1} = b^h_{t} \times d$ and $b^w_{t+1} = b^w_{t} \times d$, with $d \in (0,1)$ for shrinking or $d>1$ for scaling up. Finally, the layer trigger allows 