**True Online $TD\left(\lambda\right)$** seeks to approximate the ideal online $\lambda$-return algorithm. It seeks to invert this ideal forward-view algorithm to produce an efficient backward-view algorithm using eligibility traces. It uses dutch traces rather than accumulating traces.

Source: [Sutton and Seijen](http://proceedings.mlr.press/v32/seijen14.pdf)

**Aggregated Momentum (AggMo)** is a variant of the [classical momentum](https://paperswithcode.com/method/sgd-with-momentum) stochastic optimizer which maintains several velocity vectors with different $\beta$ parameters. AggMo averages the velocity vectors when updating the parameters. It resolves the problem of choosing a momentum parameter by taking a linear combination of multiple momentum buffers. Each of $K$ momentum buffers have a different discount factor $\beta \in \mathbb{R}^{K}$, and these are averaged for the update. The update rule is:

$$ \textbf{v}\_{t}^{\left(i\right)} = \beta^{(i)}\textbf{v}\_{t-1}^{\left(i\right)} - \nabla\_{\theta}f\left(\mathbf{\theta}\_{t-1}\right) $$

$$ \mathbf{\theta\_{t}} = \mathbf{\theta\_{t-1}} + \frac{\gamma\_{t}}{K}\sum^{K}\_{i=1}\textbf{v}\_{t}^{\left(i\right)} $$

where $v^{\left(i\right)}_{0}$ for each $i$. The vector $\mathcal{\beta} = \left[\beta^{(1)}, \ldots, \beta^{(K)}\right]$ is the dampening factor.

AggMo

Aggregated Momentum: Stability Through Passive Damping

True Online TD Lambda

Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL.

Year	2000
Data Source	Papers with Code archive (CC BY-SA)