Is Expected Sarsa is off-policy, and SARSA is just an MC estimate of Expected SARSA, why is it on-policy?

Question

So, expected SARSA defines the update as: $$ Q(s,a) = Q(s,a) +\alpha (R+ \mathbb{E}_{a\sim\pi(s')}[Q(s', a)] - Q(s,a)) $$ Where SARSA defines the update as $a'\sim\pi(s')$: $$ Q(s,a) = Q(s,a) +\alpha (R+ Q(s', a') - Q(s,a)) $$

So how is SARSA not just a MC estimate of ExpSARSA? and since MC is unbiased, why not should SARSA also be an off-policy algorithm?

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Edit: since seems like it's that clear what is the MC estimate I'm referring to, for some reason, my question is:

the two update differ only by an expected value, and $Q(s', a'\sim\pi(s')) \approx \mathbb{E}_{a\sim\pi(s')}[Q(s', a)]$, since it is a one sample monte carlo estimate, which is an unbiased estimate, so there is no reason why one should be on policy and the other off policy, since in expectation they lead to the same update

Answer 1

If you view SARSA "effectively" approximates the ExpSARSA by sampling a single action at each update step, making it a one sample MC estimate of the expected Q-values throughout only one episode, SARSA remains on-policy because its later updates in the same episode are caused by a specific choice of action sampled from its current $\epsilon$-greedy policy reflecting its all previous specific chosen actions' cumulative contributions. Further SARSA's later episode's behavior policy will be influenced by previous episodes via the feedback loop between policy and Q-values.

In contrast, in MC methods the policy is typically fixed within every episode and waits until the entire episode is completed to update the focused Q-values of a certain state-action pair based on empirical returns starting from the said state-action chain to the end of the episode. Therefore SARSA cannot be simply viewed as one sample MC estimate of ExpSARSA.