527.527. Multi-Armed Bandit Recommendations

### Problem: Multi-Armed Bandit Recommendations

In this problem, we will implement a multi-armed bandit-based recommendation system using deep learning. The goal is to predict the next item that a user will interact with based on their past interactions and the rewards associated with each item.

**Example:** Suppose we have a multi-armed bandit where items are represented as arms and rewards are associated with each arm. Our model should predict that the user will interact with arm 6 next, given that they have interacted with arm 1 and received a reward of 10, and that arm 6 has a higher expected reward than arm 5.

**Constraints:** The input will be a list of user interactions, where each interaction is a list of item IDs and rewards. The output will be a list of item IDs that the user is likely to interact with next. The model should be able to handle multi-armed bandits of varying sizes and should be able to learn from the rewards associated with each item.