SARSA learns a near-optimal policy while exploring, whereas Q-learning learns the optimal policy directly. Off-policy SARSA learns action values in relation to the policy it is following, whereas on-policy SARSA learns action values in relation to the policy it is following. In relation to the greedy policy, Q-Learning does it. They both converge to the real value function under some similar conditions, but at different speeds. Q-Learning takes a little longer to converge, but it may continue to learn while regulations are changed. When coupled with linear approximation, Q-Learning is not guaranteed to converge. SARSA will consider penalties from exploratory steps when approaching convergence, while Q-learning will not. If there's a chance of a significant negative reward along the ideal path, Q-learning will try to trigger it while exploring, however SARSA will try to avoid a risky optimal path and only learn to utilize it after the exploration parameters are decreased.