**Ape-X DPG** combines [DDPG](https://paperswithcode.com/method/ddpg) with distributed [prioritized experience replay](https://paperswithcode.com/method/prioritized-experience-replay) through the [Ape-X](https://paperswithcode.com/method/ape-x) architecture.

**Robust Predictable Control**, or **RPC**, is an RL algorithm for learning policies that uses only a few bits of information. RPC brings together ideas from information bottlenecks, model-based RL, and bits-back coding. The main idea of RPC is that if the agent can accurately predict the future, then the agent will not need to observe as many bits from future observations. Precisely, the agent will learn a latent dynamics model that predicts the next representation using the current representation and action. In addition to predicting the future, the agent can also decrease the number of bits by changing its behavior. States where the dynamics are hard to predict will require more bits, so the agent will prefer visiting states where its learned model can accurately predict the next state.

Robust Predictable Control

Ape-X DPG

Distributed Prioritized Experience Replay

**BezierAlign** is a feature sampling method for arbitrarily-shaped scene text recognition that exploits parameterization nature of a compact Bezier curve bounding box.  Unlike RoIAlign, the shape of sampling grid of BezierAlign is not rectangular. Instead, each column of the arbitrarily-shaped grid is orthogonal to the Bezier curve boundary of the text. The sampling points have equidistant interval in width and height, respectively, which are bilinear interpolated with respect to the coordinates.

Formally given an input feature map and Bezier curve control points, we concurrently process all the output pixels of the rectangular output feature map with size $h\_{\text {out }} \times w\_{\text {out }}$. Taking pixel $g\_{i}$ with position $\left(g\_{i w}, g\_{i h}\right)$ (from output feature map) as an example, we calculate $t$ by:

$$
t=\frac{g\_{i w}}{w\_{o u t}}
$$

We then calculate the point of upper Bezier curve boundary $tp$ and lower Bezier curve boundary $bp$. Using $tp$ and $bp$, we can linearly index the sampling point $op$ by:

$$
op=bp \cdot \frac{g\_{i h}}{h\_{\text {out }}}+tp \cdot\left(1-\frac{g\_{i h}}{h\_{\text {out }}}\right)
$$

With the position of $op$, we can easily apply bilinear interpolation to calculate the result. Comparisons among previous sampling methods and BezierAlign are shown in the Figure.

Source	Distributed Prioritized Experience Replay
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: Ape-X DPG?

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