Multimodal AI
Machine Learning
A classic reinforcement learning problem where an agent must choose between multiple actions with unknown reward distributions.
Detailed Explanation
The Multi-Armed Bandit Problem is a reinforcement learning challenge where an agent must select from several options, each with uncertain rewards, to maximize overall gains. It involves balancing exploration (trying new actions) and exploitation (choosing known rewarding actions). This problem models decision-making under uncertainty, optimizing strategies such as algorithms like ε-greedy and UCB to improve long-term rewards.
Use Cases
- •Optimizing ad placements by balancing exploration of new options and exploitation of known profitable ones.
Related Terms
Other terms in the Machine Learning category