While [standard convolution](https://paperswithcode.com/method/convolution) performs the channelwise and spatial-wise computation in one step, **Depthwise Separable Convolution**  splits the computation into two steps: [depthwise convolution](https://paperswithcode.com/method/depthwise-convolution) applies a single convolutional filter per each input channel and [pointwise convolution](https://paperswithcode.com/method/pointwise-convolution) is used to create a linear combination of the output of the depthwise convolution. The comparison of standard convolution and depthwise separable convolution is shown to the right.

Credit: [Depthwise Convolution Is All You Need for Learning Multiple Visual Domains](https://paperswithcode.com/paper/depthwise-convolution-is-all-you-need-for)

Stefano Guarnieri, Francesco Piazza, and Aurelio Uncini 
"Multilayer Feedforward Networks with Adaptive Spline Activation Function," 
 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 3, MAY 1999

Abstract — In this paper, a new adaptive spline activation function neural network (ASNN) is presented. Due to the ASNN’s high representation capabilities, networks with a small number of interconnections can be trained to solve both pattern recognition and data processing real-time problems. The main idea is to use a Catmull–Rom cubic spline as the neuron’s activation function, which ensures a simple structure suitable for both software and hardware implementation. Experimental results demonstrate improvements in terms of generalization capability
and of learning speed in both pattern recognition and data processing tasks.
Index Terms— Adaptive activation functions, function shape autotuning, generalization, generalized sigmoidal functions, multilayer
perceptron, neural networks, spline neural networks.

ASAF

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Depthwise Separable Convolution

Xception: Deep Learning With Depthwise Separable Convolutions

**AlphaStar** is a reinforcement learning agent for tackling the game of Starcraft II. It learns a policy $\pi\_{\theta}\left(a\_{t}\mid{s\_{t}}, z\right) = P\left[a\_{t}\mid{s\_{t}}, z\right]$ using a neural network for parameters $\theta$ that receives observations $s\_{t} = \left(o\_{1:t}, a\_{1:t-1}\right)$ as inputs and chooses actions as outputs. Additionally, the policy conditions on a statistic $z$ that summarizes a strategy sampled from human data such as a build order [1].

AlphaStar uses numerous types of architecture to incorporate different types of features. Observations of player and enemy units are processed with a [Transformer](https://paperswithcode.com/method/transformer). Scatter connections are used to integrate spatial and non-spatial information. The temporal sequence of observations is processed by a core [LSTM](https://paperswithcode.com/method/lstm). Minimap features are extracted with a Residual Network. To manage the combinatorial action space, the agent uses an autoregressive policy and a recurrent [pointer network](https://paperswithcode.com/method/pointer-net).

The agent is trained first with supervised learning from human replays. Parameters are subsequently trained using reinforcement learning that maximizes the win rate against opponents.  The RL algorithm is based on a policy-gradient algorithm similar to actor-critic. Updates are performed asynchronously and off-policy. To deal with this, a combination of $TD\left(\lambda\right)$ and [V-trace](https://paperswithcode.com/method/v-trace) are used, as well as a new self-imitation algorithm (UPGO).

Lastly, to address game-theoretic challenges, AlphaStar is trained with league training to try to approximate a fictitious self-play (FSP) setting which avoids cycles by computing a best response against a uniform mixture of all previous policies. The league of potential opponents includes a diverse range of agents, including policies from current and previous agents.

Image Credit: [Yekun Chai](https://ychai.uk/notes/2019/07/21/RL/DRL/Decipher-AlphaStar-on-StarCraft-II/)

####  References
1. Chai, Yekun. "AlphaStar: Grandmaster level in StarCraft II Explained." (2019).  [https://ychai.uk/notes/2019/07/21/RL/DRL/Decipher-AlphaStar-on-StarCraft-II/](https://ychai.uk/notes/2019/07/21/RL/DRL/Decipher-AlphaStar-on-StarCraft-II/)

#### Code Implementation
1. https://github.com/opendilab/DI-star

Source	Xception: Deep Learning With Depthwise Separable Convolutions
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com