From Wikipedia:
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 introduced in a technical note [1] where the alternative name SARSA was only mentioned as a footnote.
This name simply 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 "R" the agent gets for choosing this action, the state "S2" that the agent will now be in after taking that action, and finally the next action "A2" the agent will choose in its new state. Taking every letter in the quintuple (st, at, rt, st+1, at+1) yields the word SARSA.[2]
Sarsa is similar to Q-Learning in updating the Q-table
it is an on-policy algorithm
qnext, that followed, rather than assuming that the optimal action will be taken and that the largest reward, maxqnew, resulted.
Post: REINFORCEMENT LEARNING PART 2: SARSA VS Q-LEARNING (with code link)
Q(s, a) = reward(s) + alpha * max(Q(s‘)), and the SARSA update policy is Q(s, a) = reward(s) + alpha * Q(s‘, a‘). This is how SARSA is able to take into account the control policy of the agent during learning.The major difference between it and Q-Learning, is that the maximum reward for the next state is not necessarily used for updating the Q-values. Instead, a new action, and therefore reward, is selected using the same policy that determined the original action. (UNSW RL Algorithm webpage)
Learning Notes: Morvan - Reinforcement Learning, Part 3: Sarsa
原文：http://www.cnblogs.com/casperwin/p/6284594.html