**Ape-X DQN** is a variant of a [DQN](https://paperswithcode.com/method/dqn) with some components of [Rainbow-DQN](https://paperswithcode.com/method/rainbow-dqn) that utilizes distributed [prioritized experience replay](https://paperswithcode.com/method/prioritized-experience-replay) through the [Ape-X](https://paperswithcode.com/method/ape-x) architecture.

**Two-Way Dense Layer** is an image model block used in the [PeleeNet](https://paperswithcode.com/method/peleenet) architectures. Motivated by [GoogLeNet](https://paperswithcode.com/method/googlenet), the 2-way dense layer is used to get different scales of receptive fields. One way of the layer uses a 3x3 kernel size. The other way of the layer uses two stacked 3x3 [convolution](https://paperswithcode.com/method/convolution) to learn visual patterns for large objects.

Two-Way Dense Layer

Pelee: A Real-Time Object Detection System on Mobile Devices

Ape-X DQN

Distributed Prioritized Experience Replay

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings.

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