**$\epsilon$-Greedy Exploration** is an exploration strategy in reinforcement learning that takes an exploratory action with probability $\epsilon$ and a greedy action with probability $1-\epsilon$. It tackles the exploration-exploitation tradeoff with reinforcement learning algorithms: the desire to explore the state space with the desire to seek an optimal policy. Despite its simplicity, it is still commonly used as an behaviour policy $\pi$ in several state-of-the-art reinforcement learning models.

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Epsilon Greedy Exploration

**Cross-Scale Non-Local Attention**, or **CS-NL**,  is a non-local attention module for image super-resolution deep networks. It learns to mine long-range dependencies between LR features to larger-scale HR patches within the same feature map. Specifically, suppose we are conducting an s-scale super-resolution with the module, given a feature map $X$ of spatial size $(W, H)$, we first bilinearly downsample it to $Y$ with scale $s$, and match the $p\times p$ patches in $X$ with the downsampled $p \times p$ candidates in $Y$ to obtain the [softmax](https://paperswithcode.com/method/softmax) matching score. Finally, we conduct deconvolution.on the score by weighted adding the patches of size $\left(sp, sp\right)$ extracted from $X$. The obtained $Z$ of size $(sW, sH)$ will be $s$ times super-resolved than $X$.

Year	2000
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