SARSA
SARSA (State-Action-Reward-State-Action) is a reinforcement learning algorithm used for solving Markov Decision Processes (MDPs). It is an on-policy temporal-difference learning method that updates the value of state-action pairs based on the actual actions taken by the current policy, learning the Q-values to optimize decision-making in sequential environments.
Developers should learn SARSA when building reinforcement learning systems where the agent must learn from its own actions in real-time, such as in robotics, game AI, or autonomous systems. It is particularly useful in scenarios where exploration and exploitation must be balanced, as it directly learns from the policy being followed, making it suitable for applications like adaptive control or safe decision-making in dynamic environments.