**REINFORCE** is a Monte Carlo variant of a policy gradient algorithm in reinforcement learning. The agent collects samples of an episode using its current policy, and uses it to update the policy parameter $\theta$. Since one full trajectory must be completed to construct a sample space, it is updated as an off-policy algorithm.

$$ \nabla\_{\theta}J\left(\theta\right) = \mathbb{E}\_{\pi}\left[G\_{t}\nabla\_{\theta}\ln\pi\_{\theta}\left(A\_{t}\mid{S\_{t}}\right)\right]$$

Image Credit: [Tingwu Wang](http://www.cs.toronto.edu/~tingwuwang/REINFORCE.pdf)

**SRGAN Residual Block** is a residual block used in the [SRGAN](https://paperswithcode.com/method/srgan) generator for image super-resolution. It is similar to standard [residual blocks](https://paperswithcode.com/method/residual-block), although it uses a [PReLU](https://paperswithcode.com/method/prelu) activation function to help training (preventing sparse gradients during [GAN](https://paperswithcode.com/method/gan) training).

SRGAN Residual Block

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

REINFORCE

**GrowNet** is a novel approach to combine the power of gradient boosting to incrementally build complex deep neural networks out of shallow components. It introduces a versatile framework that can readily be adapted for a diverse range of machine learning tasks in a wide variety of domains.

Year	1999
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