SARSA Algorithm

SARSA (State-Action-Reward-State-Action) is a reinforcement learning algorithm used to learn a policy that maximizes the expected return. It is an on-policy algorithm, meaning it evaluates and improves the policy that is used to make decisions. SARSA updates its action-value function based on the action taken in the current state and the next state, allowing for more exploratory behavior compared to off-policy methods like Q-learning. Common use cases include robotics, game playing, and any scenario where an agent learns to make decisions through trial and error in an environment.