The Q-value in reinforcement learning (RL) represents the expected cumulative reward that can be obtained by taking a specific action in a given state, and then following a particular policy. Q-values are used to evaluate actions and help the agent determine which actions are most likely to lead to higher rewards.
The Q-value for a state-action pair is updated iteratively during the learning process, typically using the Q-learning algorithm. The update is based on the observed rewards from actions and the estimated future rewards from subsequent actions. The goal is for the agent to learn the optimal Q-values that guide it to the best actions.
For example, in a navigation task, the Q-value for a state-action pair (e.g., "move forward in state X") would represent the expected future rewards from moving forward in state X, considering both immediate rewards and future rewards from subsequent actions. Learning the Q-values is crucial for developing an effective policy.