**TorchBeast** is a platform for reinforcement learning (RL) research in PyTorch. It implements a version of the popular [IMPALA](https://paperswithcode.com/method/impala) algorithm for fast, asynchronous, parallel training of RL agents.

Genetic Algorithms are search algorithms that mimic Darwinian biological evolution in order to select and propagate better solutions.

Genetic Algorithms and the Traveling Salesman Problem a historical Review

TorchBeast

TorchBeast: A PyTorch Platform for Distributed RL

**EdgeBoxes** is an approach for generating object bounding box proposals directly from edges. Similar to segments, edges provide a simplified but informative representation of an image. In fact, line drawings of an image can accurately convey the high-level information contained in an image
using only a small fraction of the information. 

The main insight behind the method is the observation: the number of contours wholly enclosed by a bounding box is indicative of the likelihood of the box containing an object. We say a contour is wholly enclosed by a box if all edge pixels belonging to the contour lie within the interior of the box. Edges tend to correspond to object boundaries, and as such boxes that tightly enclose a set of edges are likely to contain an object. However, some edges that lie within an object’s bounding box may not be part of the contained object. Specifically, edge pixels that belong to contours straddling the box’s boundaries are likely to correspond to objects or structures that lie outside the box.

Source: [Zitnick and Dollar](https://pdollar.github.io/files/papers/ZitnickDollarECCV14edgeBoxes.pdf)

Source	TorchBeast: A PyTorch Platform for Distributed RL
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com