What role does the Q-table play in Reinforcement Learning?

The Q-table plays a crucial role in reinforcement learning by mapping state-action values. In reinforcement learning, an agent learns to make decisions by interacting with an environment. The Q-table is used to represent the expected utility of taking a specific action in a particular state, essentially acting as a knowledge base for the agent's policies.

Each entry in the Q-table corresponds to a state-action pair, and it holds the value of what can be expected in terms of future rewards if the agent takes that action from that state. As the agent explores the environment and receives feedback in the form of rewards, it updates this Q-value to improve its decision-making process. This method enables the agent to learn optimal policies over time, helping it choose actions that maximize its cumulative rewards.

While other options might mention aspects of reinforcement learning, such as storing experiences or rewards, they do not accurately capture the primary function of the Q-table, which is specifically about mapping the values of actions in relation to states. Therefore, the focus on state-action values makes this answer the most accurate representation of the Q-table's role in reinforcement learning.