The SARSA algorithm, introduced by Richard Sutton and Andrew Barto in the early 1990s, is an on-policy reinforcement learning method that learns policies in real-time by evaluating state-action transitions. Its safe exploration and adaptability make it ideal for dynamic and complex environments, such as traffic systems and rescue operations.
