State–action–reward–state–action

State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery and Niranjan in a technical note with the name "Modified Connectionist Q-Learning" (MCQ-L). The alternative name SARSA, proposed by Rich Sutton, was only mentioned as a footnote.

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This name reflects the fact that the main function for updating the Q-value depends on the current state of the agent "S₁", the action the agent chooses "A₁", the reward "R₂" the agent gets for choosing this action, the state "S₂" that the agent enters after taking that action, and finally the next action "A₂" the agent chooses in its new state. The acronym for the quintuple (S_t, A_t, R_t+1, S_t+1, A_t+1) is SARSA. Some authors use a slightly different convention and write the quintuple (S_t, A_t, R_t, S_t+1, A_t+1), depending on which time step the reward is formally assigned. The rest of the article uses the former convention.

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