What does the Bellman equation relate to in Reinforcement Learning?

The Bellman equation is fundamentally important in Reinforcement Learning as it captures the relationship between the value of a state under a given policy and the expected rewards of taking actions from that state, followed by future actions according to the same policy. Specifically, it describes how the value of a state reflects not only the immediate reward received by taking an action in that state but also the value of the subsequent states that can be reached from that action.

In this context, the Bellman equation lays the groundwork for understanding the concept of value functions, which are essential for evaluating how good a particular state is when following a specific policy. This evaluation enables agents to predict future rewards and make decisions that will maximize their total reward over time. Thus, option C accurately encapsulates the core function of the Bellman equation in Reinforcement Learning.