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@@ -301,3 +301,4 @@ My LeetCode solutions with Chinese explanation. 我的LeetCode中文题解。
 | 829 |[Consecutive Numbers Sum](https://leetcode.com/problems/consecutive-numbers-sum/)|[C++](solutions/829.%20Consecutive%20Numbers%20Sum.md)|Hard|  |
 | 905 |[Sort Array By Parity](https://leetcode.com/problems/sort-array-by-parity/)|[C++](solutions/905.%20Sort%20Array%20By%20Parity.md)|Easy|  |
 | 946 |[Validate Stack Sequences](https://leetcode.com/problems/validate-stack-sequences/)|[C++](solutions/946.%20Validate%20Stack%20Sequences.md)|Medium|  |
+| 1155 |[Number of Dice Rolls With Target Sum](https://leetcode.com/problems/number-of-dice-rolls-with-target-sum/)|[C++](solutions/1155.%20Number%20of%20Dice%20Rolls%20With%20Target%20Sum.md)|Medium|  |
diff --git a/solutions/1155. Number of Dice Rolls With Target Sum.md b/solutions/1155. Number of Dice Rolls With Target Sum.md
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+# [1155. Number of Dice Rolls With Target Sum](https://leetcode.com/problems/number-of-dice-rolls-with-target-sum/)
+
+# 思路
+仔细分析一下可知，d个骰子的和为 target 的情况数取决于 d-1 个骰子的和为target-1、target-2...target-f的情况数。所以这题是一个典型的动态规划题。状态定义为
+```
+dp[i][j]: i个骰子的和为j的情况数，dp[0][0]=1
+```
+状态转移方程为：
+```
+dp[i][j] = dp[i-1][j-1] + dp[i-1][j-2] +...+ dp[i-1][j-f]
+```
+
+
+## 思路一、自上而下
+
+动态规划一般都可以转换成带记忆数组的递归，这题也不例外。
+
+时间复杂度O(d * f * target)，空间复杂度O(d * target)
+
+## 思路二、自下而上
+更常见的是自下而上，而且我们还可以使用滚动数组对空间进行优化。
+
+时间复杂度O(d * f * target)，空间复杂度O(target)
+
+## 思路三
+思路二一共有三层循环，可以进一步优化时间。
+
+回顾一下状态转移方程，
+```
+dp[i][j] =                dp[i-1][j-1] + dp[i-1][j-2] +...+   dp[i-1][j-f]
+
+j++后:
+dp[i][j+1] = dp[i-1][j] + dp[i-1][j-1] +...+ dp[i-1][j-f+1]
+```
+所以说连续两次运行第三次循环是有大量重复的，所以第三层循环不是必须的。
+
+可以这样考虑，`dp[i][j]` 的计算就是求一个大小为 f 的在数组`dp[i-1]`上的滑动窗口的元素和，思路二的第三层循环每次都从头到尾累加窗口内的元素，但事实上我们可以维护一个变量s代表窗口内元素的和，当窗口向右滑动一步时我们只需要将s加上窗口右端元素然后减去刚刚离开窗口左端的元素即可。
+
+时间复杂度O(d * target)，空间复杂度O(target)
+
+# C++
+
+## 思路一
+``` C++
+class Solution {
+private:
+    vector<vector<int>>dp = vector<vector<int>>(31, vector<int>(1001, -1));
+public:
+    int numRollsToTarget(int d, int f, int target) {
+        if(target < 0 || target > d * f) return 0;
+        if(d == 0) return target == 0 ? 1 : 0;
+        
+        if(dp[d][target] >= 0) return dp[d][target];
+        int res = 0;
+        for(int i = 1; i <=f; i++){
+            res += numRollsToTarget(d - 1, f, target - i);
+            res %= 1000000007;
+        }
+        
+        dp[d][target] = res;
+        return res;
+    }
+};
+```
+
+## 思路二
+``` C++
+class Solution {    
+public:
+    int numRollsToTarget(int d, int f, int target) {
+        if(target > d * f) return 0; // 会大大减少测试时间
+
+        vector<int>dp(target+1, 0);
+        dp[0] = 1;
+        for(int i = 1; i <= d; i++)
+            for(int j = target; j >= 0; j--){ // 注意这里要反向遍历!
+                int max_k = min(j, f); dp[j] = 0;
+                for(int k = 1; k <= max_k; k++){
+                    dp[j] += dp[j-k];
+                    dp[j] %= 1000000007;
+                }
+            }
+        return dp[target];
+    }
+};
+```
+
+## 思路三
+``` C++
+class Solution {
+public:
+    int numRollsToTarget(int d, int f, int target) {
+        const int M = 1000000007;
+        
+        // dp表示dp[i][...], pre代表dp[i-1][...]
+        vector<int> dp(target + 1, 0), pre(target + 1, 0);
+        dp[0] = 1;
+        long long s = 0;
+        for(int i = 1; i <= d; i++){
+            for(int t = 0; t <= target; t++) pre[t] = dp[t];
+            
+            s = 0;
+            for(int j = 0; j <= target; j++){
+                dp[j] = s;
+                s += pre[j]; // 累加上窗口右端元素
+                if(j >= f) s += M - pre[j - f]; // 减去刚刚离开窗口左端的元素
+                s %= M;
+            }
+        } 
+        return dp[target];
+    }
+};
+```
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