A **Stochastic Dueling Network**, or **SDN**, is an architecture for learning a value function $V$. The SDN learns both $V$ and $Q$ off-policy while maintaining consistency between the two estimates. At each time step it outputs a stochastic estimate of $Q$ and a deterministic estimate of $V$.

**WaveGrad DBlocks** are used to downsample the temporal dimension of noisy waveform in [WaveGrad](https://paperswithcode.com/method/wavegrad). They are similar to UBlocks except that only one [residual block](https://paperswithcode.com/method/residual-block) is included. The dilation factors are 1, 2, 4 in the main branch. Orthogonal initialization is used.

WaveGrad DBlock

WaveGrad: Estimating Gradients for Waveform Generation

Stochastic Dueling Network

Sample Efficient Actor-Critic with Experience Replay

A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency.  They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-downsampling), In-Place Activated BatchNorm, Blocks selection and squeeze-and-excitation layers.

Source	Sample Efficient Actor-Critic with Experience Replay
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com