It is an application preview web app.
Explorer in AI: Applications
The major examples of the use of Explorer in AI include the following:
1. Reinforcement Learning
In reinforcement learning (RL), an agent learns to maximize a reward signal. Explorers assist RL agents in understanding the environment and taking actions that can help maximize the reward signals. To stimulate exploration, the agent may utilize an epsilon-greedy explorer that randomly selects actions with a probability.
2. Evolutionary algorithms (EA)
solve problems through natural selection. EAs generate multiple solutions and evolve them using mutation as well as crossover operators. Explorers diversify EA populations and avoid premature convergence. The EA may utilize a random explorer to disrupt current solutions to produce new ones.
3. Active Learning
A model is trained on a small labeled dataset, then selects the most informative samples from a large unlabeled dataset to be tagged by an expert. Explorers pick the best examples for active learning. For example, the active learning algorithm may employ an uncertainty explorer to pick the most uncertain samples.
4. Data Augmentation
In data augmentation, random transformations are applied to the original dataset in order to generate new training examples. Here, explorers are used to generate different and realistic transformations. For example, images could be rotated, translated, and flipped at random as part of the data augmentation algorithm’s exploration phase.
5. Adversarial Attacks
Adversarial attacks on ML models are attempts to trick the models by introducing subtle changes to the input data.. Explorers help in adversarial attacks to identify the best disturbances. For instance, the attacker may utilize a gradient-based explorer to alter the input in the loss function’s gradient.
