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Q-Values from State Values

MediumReinforcement Learning 📚 RL 1: Foundations

Problem Statement

Given state values V, recover the action values for one state. Each action a provides reward[a] and a next-state distribution P[a] (P[a][s']):

$Q(s,a) = reward[a] + \gamma \sum_{s'} P[a][s']\, V(s')$

Implement q_from_v(reward, P, gamma, V) returning the list of Q-values, one per action.

Example:

Input:

q_from_v([1.0, 0.0], [[1.0, 0.0], [0.0, 1.0]], 0.9, [0.0, 10.0])

Output:

[1.0, 9.0]

Reasoning:

Action 0 Calculation: For the first action, the immediate reward is $1.0$ . The expected value of the next state is computed by weighting the state values $V = [0.0, 10.0]$ by the transition probabilities $P[0] = [1.0, 0.0]$ , resulting in $1.0 \times 0.0 + 0.0 \times 10.0 = 0.0$ .
Action 0 Q-Value: The Q-value is the sum of the immediate reward and the discounted expected future value: $1.0 + 0.9 \times 0.0 = 1.0$ .
Action 1 Calculation: For the second action, the immediate reward is $0.0$ . The expected value of the next state is computed using the transition probabilities $P[1] = [0.0, 1.0]$ , resulting in $0.0 \times 0.0 + 1.0 \times 10.0 = 10.0$ .
Action 1 Q-Value: The Q-value is the sum of the immediate reward and the discounted expected future value: $0.0 + 0.9 \times 10.0 = 9.0$ .
The final output is [1.0, 9.0]

Constraints:

len(reward) == len(P) (one row per action).
Each P[a] aligns with V and sums to 1.
Return a list of floats.

More from RL 1: Foundations

GAE Over a Full TrajectoryHard PPO Clipped Objective (Single Sample)Medium TD Residuals for GAEMedium Return Standardization BaselineMedium REINFORCE Loss TermMedium

1. Background Knowledge

In reinforcement learning, the state-value function $V(s)$ estimates the expected return starting from state $s$ and following a policy $\pi$ . The action-value function $Q(s,a)$ estimates the expected return starting from state $s$ , taking action $a$ , and then following $\pi$ . These two functions are linked by the Bellman expectation equation:

Q(s,a) = R(s,a) + \gamma \sum_{s'} P(s' \mid s,a)\, V(s')

Here, $R(s,a)$ is the immediate reward, $\gamma \in [0,1]$ is the discount factor, and $P(s' \mid s,a)$ is the probability of transitioning to next state $s'$ . The sum represents the expected discounted value of all possible next states under the transition distribution for action $a$ .

This relationship is foundational in dynamic programming methods for RL. Given a fixed policy (or a fixed $V$ ), you can compute $Q$ directly via this expectation. Conversely, given $Q$ , you can recover $V(s) = \sum_a \pi(a \mid s)\, Q(s,a)$ . The problem you are solving is the forward direction: from a known $V$ , compute $Q$ for every action in a single state.

2. Algorithm Approach

The core pattern is a weighted sum over next states. For each action $a$ :

Start with the immediate reward $R(a)$ .
Compute the expected next-state value: $\sum_{s'} P(a, s') \cdot V(s')$ . This is a dot product between the transition probability vector for action $a$ and the state-value vector $V$ .
Multiply that expectation by $\gamma$ and add it to the immediate reward.

The result is $Q(a)$ . Repeat for every action. There is no iterative procedure or matrix inversion needed—this is a single-pass computation over the action list.

3. Step-by-Step Strategy

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solution.py

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