This method works as a two-levels optimization algorithm.
The outmost layer uses Grammatical evolution to evolve a grammar to build the agent.
Then, [Q-learning](https://paperswithcode.com/method/q-learning) is used the fitness evaluation phase to allow the agent to learn to perform online learning.

**R_INLINE_MATH_1 Regularization** is a regularization technique and gradient penalty for training [generative adversarial networks](https://paperswithcode.com/methods/category/generative-adversarial-networks). It penalizes the discriminator from deviating from the Nash Equilibrium via penalizing the gradient on real data alone: when the generator distribution produces the true data distribution and the discriminator is equal to 0 on the data manifold, the gradient penalty ensures that the discriminator cannot create a non-zero gradient orthogonal to the data manifold without suffering a loss in the [GAN](https://paperswithcode.com/method/gan) game.

This leads to the following regularization term:

$$ R\_{1}\left(\psi\right) = \frac{\gamma}{2}E\_{p\_{D}\left(x\right)}\left[||\nabla{D\_{\psi}\left(x\right)}||^{2}\right] $$

R1 Regularization

Which Training Methods for GANs do actually Converge?

Grammatical evolution + Q-learning

Evolutionary learning of interpretable decision trees

ALBEF introduces a contrastive loss to align the image and text representations before fusing them through cross-modal attention. This enables more grounded vision and language representation learning. ALBEF also doesn't require bounding box annotations. The model consists of an image encode, a text encoder, and a multimodal encoder. The image-text contrastive loss helps to align the unimodal representations of an image-text pair before fusion. The image-text matching loss and a masked language modeling loss are applied to learn multimodal interactions between image and text. In addition, momentum distillation is used to generate pseudo-targets. This improves learning with noisy data.

Source	Evolutionary learning of interpretable decision trees
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com