Our next step in understanding RL will be for us to look at the contextual bandit problem. A contextual bandit is the multi-armed bandit problem, with multiple bandits each producing different rewards. This type of problem has many applications in online advertising, where each user is thought of as a different bandit, with the goal being to present the best advertisement for that user. To model the context of the bandit, and which bandit it is, we add the concept of state. Where we now interpret state to represent each of our different bandits. The following diagram shows the addition of state in the Contextual Bandit problem and where it lies on our path to glory:
Stateless, Contextual and Full RL models
You can see in the preceding diagram that we now need to determine the state before evaluating an action. If you recall from earlier, the Value...
