Neural Exploitation and Exploration of Contextual Bandits

Understand

In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits.

• Contextual multi-armed bandits have been studied for decades with various applications.
• To solve the exploitation-exploration trade-off in bandits, there are three main techniques: epsilon-greedy, Thompson Sampling (TS), and Upper Confidence Bound (UCB).
• In recent literature, a series of neural bandit algorithms have been proposed to adapt to the non-linear reward function, combined with TS or UCB strategies for exploration.