Writing action-value function in terms of state-value function for a Markov decision process I am working through Sutton and Barto's text on reinforcement learning (2nd edition), and am stuck on exercise 3.13 (p. 58). The exercise is to write $q_\pi$ in terms of $v_\pi$ and $p(s',r\mid s,a)$, where $q_\pi$ is the action-value function defined by $$q_\pi(s,a) := \mathbb E_\pi [G_t \mid S_t=s, A_t=a];$$ $v_\pi$ is the state-value function defined by $$v_\pi(s) := \mathbb E_\pi[G_t \mid S_t=s];$$ and $G_t$ is the discounted return $$G_t := \sum_{k=0}^\infty \gamma^k R_{t+k+1}.$$
My attempt so far is to write $G_t = R_{t+1} + \gamma G_{t+1}$ so that $$q_\pi(s,a) = \mathbb E_\pi[R_{t+1} \mid S_t=s,A_t=a] + \gamma \mathbb E_\pi[G_{t+1} \mid S_t=s,A_t=a].$$
Now $\mathbb E_\pi[G_{t+1} \mid S_t=s,A_t=a]$ becomes:


*

*$\sum_g g \Pr(G_{t+1}=g\mid S_t=s,A_t=a)$ (definition of expected value)

*$\sum_g g \sum_{s'} \Pr(G_{t+1}=g, S_{t+1}=s'\mid S_t=s,A_t=a)$ (law of total probability)

*$\sum_g g \sum_{s'} \Pr(G_{t+1}=g \mid S_t=s,A_t=a,S_{t+1}=s') \Pr(S_{t+1}=s' \mid S_t=s,A_t=a)$


*

*(definition of conditional probability; the expression was too long so I've placed this parenthetical on the line below)


*$\sum_{s'} \Pr(S_{t+1}=s'\mid S_t=s,A_t=a) \sum_g g \Pr(G_{t+1}=g\mid S_t=s,A_t=a,S_{t+1}=s')$


*

*(rearranging sums)


*$\sum_{s'}\Pr(S_{t+1}=s'\mid S_t=s,A_t=a) \mathbb E_\pi[G_{t+1} \mid S_t=s,A_t=a,S_{t+1}=s']$


*

*(definition of expected value)



In turn, $\Pr(S_{t+1}=s'\mid S_t=s,A_t=a)$ can be written as $\sum_r p(s',r\mid s,a)$ (this is equation 3.5 in the book).
Similarly $\mathbb E_\pi[R_{t+1} \mid S_t=s,A_t=a]$ can be written as $\sum_{s'} \Pr(S_{t+1}=s'\mid S_t=s,A_t=a) \mathbb E_\pi[R_{t+1} \mid S_t=s,A_t=a,S_{t+1}=s']$ where $\mathbb E_\pi[R_{t+1} \mid S_t=s,A_t=a,S_{t+1}=s']$ can be written in terms of $p(s',r\mid s,a)$ (using equation 3.6 in the book).
So the only problem left is the expression $\mathbb E_\pi[G_{t+1} \mid S_t=s,A_t=a,S_{t+1}=s']$. If I squint my eyes, it looks like $v_\pi(s')$, and indeed, looking around, I see formulas indicating that it should be $v_\pi(s')$ (e.g. PDF page 35 of these slides, page 13 of these slides, and the equation given in this section). But I don't see how to convince myself that this is the case. Sutton and Barto write that "the probability of each possible value for $S_t$ and $R_t$ depends only on the immediately preceding state and action, $S_{t-1}$ and $A_{t-1}$, and, given them, not at all on earlier states and actions" (p. 49). But here, we only have $S_{t+1}$ and not $A_{t+1}$, so we can't just ignore the "$S_t=s,A_t=a$" part. Given the policy $\pi$, the action $A_{t+1}$ is completely determined by $S_{t+1}$, so my attempt is to write:


*

*$\mathbb E_\pi[G_{t+1} \mid S_t=s,A_t=a,S_{t+1}=s']$

*$\sum_g g \Pr(G_{t+1}=g \mid S_t=s,A_t=a,S_{t+1}=s')$ (definition of expected value)

*$\sum_g g \sum_{a'} \Pr(G_{t+1}=g,A_{t+1}=a' \mid S_t=s,A_t=a,S_{t+1}=s')$ (law of total probability)

*$\sum_g g \sum_{a'} \Pr(G_{t+1}=g \mid S_t=s,A_t=a,S_{t+1}=s',A_{t+1}=a')\pi(a'\mid s')$


*

*(definition of conditional probability)


*$\sum_g g \sum_{a'} \Pr(G_{t+1}=g \mid S_{t+1}=s',A_{t+1}=a')\pi(a'\mid s')$ (Markov property)

*$\sum_g g \sum_{a'} \Pr(G_{t+1}=g,A_{t+1}=a' \mid S_{t+1}=s')$ (definition of conditional probability)

*$\sum_g g \Pr(G_{t+1}=g\mid S_{t+1}=s')$ (law of total probability)

*$\mathbb E_\pi[G_{t+1} \mid S_{t+1}=s']$ (definition of expected value)


Is this reasoning valid?
My other confusion is that, supposing $\mathbb E_\pi[G_{t+1} \mid S_t=s,A_t=a,S_{t+1}=s'] = \mathbb E_\pi[G_{t+1} \mid S_{t+1}=s']$,  it seems like we could also say that $\mathbb E_\pi[R_t\mid S_{t-1}=s,A_{t-1}=a,S_t=s'] = \mathbb E_\pi[R_t \mid S_t=s']$. But if that's the case, I'm confused about why Sutton and Barto derive an expression for $r(s,a,s'):=\mathbb E_\pi[R_t\mid S_{t-1}=s,A_{t-1}=a,S_t=s']$ rather than one for $r(s')$ (which can be defined to equal $\mathbb E_\pi[R_t\mid S_t=s']$). In other words my question is something like "if the $s$ and $a$ in $r(s,a,s')$ don't matter, why include them in the notation?"
 A: 
Is this reasoning valid?

I think this is correct.

My other confusion is that, supposing $\mathbb E_\pi[G_{t+1} \mid S_t=s,A_t=a,S_{t+1}=s'] = \mathbb E_\pi[G_{t+1} \mid S_{t+1}=s']$,  it seems like we could also say that $\mathbb E_\pi[R_t\mid S_{t-1}=s,A_{t-1}=a,S_t=s'] = \mathbb E_\pi[R_t \mid S_t=s']$.

I think my confusion was that I was forgetting that $G_{t+1}$ actually starts at $R_{t+2}$, i.e. $G_{t+1} = R_{t+2} + \gamma R_{t+3} + \cdots$.  So I think we could say $\mathbb E_\pi[R_{t+2} \mid S_t=s,A_t=a,S_{t+1}=s'] = \mathbb E_\pi[R_{t+2} \mid S_{t+1}=s']$, which, shifting the time index down by one, is the same as $\mathbb E_\pi[R_{t+1} \mid S_{t-1}=s,A_{t-1}=a,S_t=s'] = \mathbb E_\pi[R_{t+1} \mid S_t=s']$. But we can't say $\mathbb E_\pi[R_t\mid S_{t-1}=s,A_{t-1}=a,S_t=s'] = \mathbb E_\pi[R_t \mid S_t=s']$ which is why Sutton and Barto want to derive the expression for $r(s,a,s')$.
If I was doing the exercise today, I would do it like this:
$q_\pi(s,a) := \mathbb E_\pi[G_t \mid S_t=s, A_t=a]$ becomes:


*

*$\sum_g g \Pr(G_t = g\mid S_t=s, A_t=a)$ (definition of expectation)

*$\sum_g g \sum_{s'} \Pr(G_t=g, S_{t+1}=s' \mid S_t=s, A_t=a)$ (law of total probability)

*$\sum_{s'} \sum_g g  \Pr(G_t=g, S_{t+1}=s' \mid S_t=s, A_t=a)$ (reorder sums)

*$\sum_{s'} \sum_g g  \Pr(S_{t+1}=s'\mid S_t=s, A_t=a) \Pr(G_t=g \mid S_t=s, A_t=a, S_{t+1}=s')$ (definition of conditional probability)

*$\sum_{s'} \Pr(S_{t+1}=s'\mid S_t=s, A_t=a) \sum_g g  \Pr(G_t=g \mid S_t=s, A_t=a, S_{t+1}=s')$ (factor)

*$\sum_{s'} \Pr(S_{t+1}=s'\mid S_t=s, A_t=a) \mathbb E_\pi[G_t \mid S_t=s, A_t=a, S_{t+1}=s']$ (definition of expectation)

*$\sum_{s'} \Pr(S_{t+1}=s'\mid S_t=s, A_t=a) \mathbb E_\pi[R_{t+1} + \gamma G_{t+1} \mid S_t=s, A_t=a, S_{t+1}=s']$ (definition of return)

*$\sum_{s'} \Pr(S_{t+1}=s'\mid S_t=s, A_t=a) (\mathbb E_\pi[R_{t+1}\mid S_t=s, A_t=a, S_{t+1}=s'] + \gamma\mathbb E_\pi[ G_{t+1} \mid S_t=s, A_t=a, S_{t+1}=s'])$


*

*(linearity of expectation)


*$\sum_{s'} p(s'\mid s,a) (r(s,a,s') + \gamma\mathbb E_\pi[ G_{t+1} \mid S_t=s, A_t=a, S_{t+1}=s'])$ (equations 3.4 and 3.6 in the book)

*$\sum_{s'} p(s'\mid s,a) (r(s,a,s') + \gamma v_\pi(s'))$ (using reasoning given in the question)


This is probably the "simplest" form of $q_\pi$ which uses $v_\pi$, but the exercise actually says to use "the four-argument $p$", so we can expand $\sum_{s'} p(s'\mid s,a) (r(s,a,s') + \gamma v_\pi(s'))$ to $$\sum_{s'} \left(\sum_r p(s',r\mid s,a)\right) \left(\sum_r r \frac{p(s',r\mid s,a)}{\sum_{r'} p(s',r'\mid s,a)} + \gamma v_\pi(s')\right)$$ which can be simplified to $$\sum_{s',r} p(s',r\mid s,a) (r + \gamma v_\pi(s'))$$
