SARSA

SARSA (State-Action-Reward-State-Action) is a reinforcement learning method where an agent learns to make decisions by trial and error. It updates its knowledge based on the experience of taking an action in a particular state, receiving a reward, and then moving to a new state and choosing the next action. Over time, it learns which actions lead to better outcomes by continuously adjusting its strategy, aiming to maximize cumulative rewards. Essentially, SARSA helps an agent improve its decisions through ongoing feedback from the environment, balancing exploration of new options and exploitation of known good ones.