TalvexAI Blog • June 2026

Dynamic Programming: A Dice Game Problem

Imagine you're playing a dice game where you need to maximize your score by choosing the best sequence of rolls from a given set. This problem can be efficiently solved using dynamic programming, which is an algorithmic technique for solving complex problems by breaking them down into simpler subproblems and storing their solutions.

Problem Statement

In this dice game problem, you are given a sequence of dice rolls. Each roll results in a number from 1 to 6. The goal is to maximize the total score by choosing the best sequence of rolls.

For example, if the sequence of rolls is [2, 4, 3, 5], the optimal strategy might be to choose the first two rolls (2 and 4) since they sum up to 6, which is the maximum possible score for a single roll. However, choosing all four rolls would result in a total score of 18.

Algorithmic Approach / Logic

To solve this problem using dynamic programming:

  1. Define a DP array where dp[i] represents the maximum score that can be achieved with up to thei-th roll.
  2. Initialize the base case: dp[0] = 0 since no rolls result in any score.
  3. For each roll i, update the DP array by considering all possible outcomes of rolling a dice (1 to 6). Update dp[i] as the maximum of current value and dp[j] + outcome, where j
  4. The final answer will be stored in dp[n-1], where n is the total number of rolls.

Code Implementation

def max_score(rolls):
    n = len(rolls)
    dp = [0] * n
    for i in range(n):
        dp[i] = rolls[i]
        for j in range(i):
            dp[i] = max(dp[i], dp[j] + rolls[i])
    return dp[n-1]

This function initializes the DP array and iterates through each roll, updating the maximum score that can be achieved.

Complexity Analysis

The time complexity of this dynamic programming solution is O(n * 6), where n is the number of rolls. This is because for each roll, we iterate through all possible previous rolls (0 to n-1), and there are 6 possible outcomes per roll.

The space complexity is also O(n) as we use a DP array of size n to store the maximum scores up to each roll.

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