SARSA (State-Action-Reward-State-Action)

SARSA, or State-Action-Reward-State-Action, is a method used in reinforcement learning, a type of artificial intelligence. It helps an agent learn how to make decisions by interacting with an environment. The agent observes its current state, takes an action, receives a reward, and then observes the next state before choosing its next action. By repeating this process, the agent gradually learns which actions lead to better rewards over time. SARSA is beneficial as it updates its strategy based on the actual actions taken, making it adaptable and grounded in real experiences.