diff --git a/codes/kotlin/chapter_dynamic_programming/climbing_stairs_backtrack.kt b/codes/kotlin/chapter_dynamic_programming/climbing_stairs_backtrack.kt
new file mode 100644
index 000000000..03af6060e
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/climbing_stairs_backtrack.kt
@@ -0,0 +1,44 @@
+/**
+ * File: climbing_stairs_backtrack.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+/* 回溯 */
+fun backtrack(
+    choices: List<Int>,
+    state: Int,
+    n: Int,
+    res: MutableList<Int>
+) {
+    // 当爬到第 n 阶时，方案数量加 1
+    if (state == n) res[0] = res[0] + 1
+    // 遍历所有选择
+    for (choice in choices) {
+        // 剪枝：不允许越过第 n 阶
+        if (state + choice > n) continue
+        // 尝试：做出选择，更新状态
+        backtrack(choices, state + choice, n, res)
+        // 回退
+    }
+}
+
+/* 爬楼梯：回溯 */
+fun climbingStairsBacktrack(n: Int): Int {
+    val choices = mutableListOf(1, 2) // 可选择向上爬 1 阶或 2 阶
+    val state = 0 // 从第 0 阶开始爬
+    val res = ArrayList<Int>()
+    res.add(0) // 使用 res[0] 记录方案数量
+    backtrack(choices, state, n, res)
+    return res[0]
+}
+
+/* Driver Code */
+fun main() {
+    val n = 9
+
+    val res = climbingStairsBacktrack(n)
+    println("爬 $n 阶楼梯共有 $res 种方案")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/climbing_stairs_constraint_dp.kt b/codes/kotlin/chapter_dynamic_programming/climbing_stairs_constraint_dp.kt
new file mode 100644
index 000000000..4dc70a151
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/climbing_stairs_constraint_dp.kt
@@ -0,0 +1,35 @@
+/**
+ * File: climbing_stairs_constraint_dp.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+/* 带约束爬楼梯：动态规划 */
+fun climbingStairsConstraintDP(n: Int): Int {
+    if (n == 1 || n == 2) {
+        return 1
+    }
+    // 初始化 dp 表，用于存储子问题的解
+    val dp = Array(n + 1) { IntArray(3) }
+    // 初始状态：预设最小子问题的解
+    dp[1][1] = 1
+    dp[1][2] = 0
+    dp[2][1] = 0
+    dp[2][2] = 1
+    // 状态转移：从较小子问题逐步求解较大子问题
+    for (i in 3..n) {
+        dp[i][1] = dp[i - 1][2]
+        dp[i][2] = dp[i - 2][1] + dp[i - 2][2]
+    }
+    return dp[n][1] + dp[n][2]
+}
+
+/* Driver Code */
+fun main() {
+    val n = 9
+
+    val res = climbingStairsConstraintDP(n)
+    println("爬 $n 阶楼梯共有 $res 种方案")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/climbing_stairs_dfs.kt b/codes/kotlin/chapter_dynamic_programming/climbing_stairs_dfs.kt
new file mode 100644
index 000000000..ecb2345ad
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/climbing_stairs_dfs.kt
@@ -0,0 +1,29 @@
+/**
+ * File: climbing_stairs_dfs.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+/* 搜索 */
+fun dfs(i: Int): Int {
+    // 已知 dp[1] 和 dp[2] ，返回之
+    if (i == 1 || i == 2) return i
+    // dp[i] = dp[i-1] + dp[i-2]
+    val count = dfs(i - 1) + dfs(i - 2)
+    return count
+}
+
+/* 爬楼梯：搜索 */
+fun climbingStairsDFS(n: Int): Int {
+    return dfs(n)
+}
+
+/* Driver Code */
+fun main() {
+    val n = 9
+
+    val res = climbingStairsDFS(n)
+    println("爬 $n 阶楼梯共有 $res 种方案")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/climbing_stairs_dp.kt b/codes/kotlin/chapter_dynamic_programming/climbing_stairs_dp.kt
new file mode 100644
index 000000000..ed88c0bd0
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/climbing_stairs_dp.kt
@@ -0,0 +1,46 @@
+/**
+ * File: climbing_stairs_dp.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+/* 爬楼梯：动态规划 */
+fun climbingStairsDP(n: Int): Int {
+    if (n == 1 || n == 2) return n
+    // 初始化 dp 表，用于存储子问题的解
+    val dp = IntArray(n + 1)
+    // 初始状态：预设最小子问题的解
+    dp[1] = 1
+    dp[2] = 2
+    // 状态转移：从较小子问题逐步求解较大子问题
+    for (i in 3..n) {
+        dp[i] = dp[i - 1] + dp[i - 2]
+    }
+    return dp[n]
+}
+
+/* 爬楼梯：空间优化后的动态规划 */
+fun climbingStairsDPComp(n: Int): Int {
+    if (n == 1 || n == 2) return n
+    var a = 1
+    var b = 2
+    for (i in 3..n) {
+        val tmp = b
+        b += a
+        a = tmp
+    }
+    return b
+}
+
+/* Driver Code */
+fun main() {
+    val n = 9
+
+    var res = climbingStairsDP(n)
+    println("爬 $n 阶楼梯共有 $res 种方案")
+
+    res = climbingStairsDPComp(n)
+    println("爬 $n 阶楼梯共有 $res 种方案")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/climbing_stiars_dfs_mem.kt b/codes/kotlin/chapter_dynamic_programming/climbing_stiars_dfs_mem.kt
new file mode 100644
index 000000000..d6c20aa18
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/climbing_stiars_dfs_mem.kt
@@ -0,0 +1,38 @@
+/**
+ * File: climbing_stairs_dfs_mem.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+import java.util.*
+
+/* 记忆化搜索 */
+fun dfs(i: Int, mem: IntArray): Int {
+    // 已知 dp[1] 和 dp[2] ，返回之
+    if (i == 1 || i == 2) return i
+    // 若存在记录 dp[i] ，则直接返回之
+    if (mem[i] != -1) return mem[i]
+    // dp[i] = dp[i-1] + dp[i-2]
+    val count = dfs(i - 1, mem) + dfs(i - 2, mem)
+    // 记录 dp[i]
+    mem[i] = count
+    return count
+}
+
+/* 爬楼梯：记忆化搜索 */
+fun climbingStairsDFSMem(n: Int): Int {
+    // mem[i] 记录爬到第 i 阶的方案总数，-1 代表无记录
+    val mem = IntArray(n + 1)
+    Arrays.fill(mem, -1)
+    return dfs(n, mem)
+}
+
+/* Driver Code */
+fun main() {
+    val n = 9
+
+    val res: Int = climbingStairsDFSMem(n)
+    println("爬 $n 阶楼梯共有 $res 种方案")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/coin_change.kt b/codes/kotlin/chapter_dynamic_programming/coin_change.kt
new file mode 100644
index 000000000..903dacc29
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/coin_change.kt
@@ -0,0 +1,73 @@
+/**
+ * File: coin_change.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+import java.util.*
+import kotlin.math.min
+
+/* 零钱兑换：动态规划 */
+fun coinChangeDP(coins: IntArray, amt: Int): Int {
+    val n = coins.size
+    val MAX = amt + 1
+    // 初始化 dp 表
+    val dp = Array(n + 1) { IntArray(amt + 1) }
+    // 状态转移：首行首列
+    for (a in 1..amt) {
+        dp[0][a] = MAX
+    }
+    // 状态转移：其余行和列
+    for (i in 1..n) {
+        for (a in 1..amt) {
+            if (coins[i - 1] > a) {
+                // 若超过目标金额，则不选硬币 i
+                dp[i][a] = dp[i - 1][a]
+            } else {
+                // 不选和选硬币 i 这两种方案的较小值
+                dp[i][a] = min(dp[i - 1][a].toDouble(), (dp[i][a - coins[i - 1]] + 1).toDouble())
+                    .toInt()
+            }
+        }
+    }
+    return if (dp[n][amt] != MAX) dp[n][amt] else -1
+}
+
+/* 零钱兑换：空间优化后的动态规划 */
+fun coinChangeDPComp(coins: IntArray, amt: Int): Int {
+    val n = coins.size
+    val MAX = amt + 1
+    // 初始化 dp 表
+    val dp = IntArray(amt + 1)
+    Arrays.fill(dp, MAX)
+    dp[0] = 0
+    // 状态转移
+    for (i in 1..n) {
+        for (a in 1..amt) {
+            if (coins[i - 1] > a) {
+                // 若超过目标金额，则不选硬币 i
+                dp[a] = dp[a]
+            } else {
+                // 不选和选硬币 i 这两种方案的较小值
+                dp[a] = min(dp[a].toDouble(), (dp[a - coins[i - 1]] + 1).toDouble()).toInt()
+            }
+        }
+    }
+    return if (dp[amt] != MAX) dp[amt] else -1
+}
+
+/* Driver Code */
+fun main() {
+    val coins = intArrayOf(1, 2, 5)
+    val amt = 4
+
+    // 动态规划
+    var res = coinChangeDP(coins, amt)
+    println("凑到目标金额所需的最少硬币数量为 $res")
+
+    // 空间优化后的动态规划
+    res = coinChangeDPComp(coins, amt)
+    println("凑到目标金额所需的最少硬币数量为 $res")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/coin_change_ii.kt b/codes/kotlin/chapter_dynamic_programming/coin_change_ii.kt
new file mode 100644
index 000000000..683a86055
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/coin_change_ii.kt
@@ -0,0 +1,66 @@
+/**
+ * File: coin_change_ii.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+/* 零钱兑换 II：动态规划 */
+fun coinChangeIIDP(coins: IntArray, amt: Int): Int {
+    val n = coins.size
+    // 初始化 dp 表
+    val dp = Array(n + 1) { IntArray(amt + 1) }
+    // 初始化首列
+    for (i in 0..n) {
+        dp[i][0] = 1
+    }
+    // 状态转移
+    for (i in 1..n) {
+        for (a in 1..amt) {
+            if (coins[i - 1] > a) {
+                // 若超过目标金额，则不选硬币 i
+                dp[i][a] = dp[i - 1][a]
+            } else {
+                // 不选和选硬币 i 这两种方案之和
+                dp[i][a] = dp[i - 1][a] + dp[i][a - coins[i - 1]]
+            }
+        }
+    }
+    return dp[n][amt]
+}
+
+/* 零钱兑换 II：空间优化后的动态规划 */
+fun coinChangeIIDPComp(coins: IntArray, amt: Int): Int {
+    val n = coins.size
+    // 初始化 dp 表
+    val dp = IntArray(amt + 1)
+    dp[0] = 1
+    // 状态转移
+    for (i in 1..n) {
+        for (a in 1..amt) {
+            if (coins[i - 1] > a) {
+                // 若超过目标金额，则不选硬币 i
+                dp[a] = dp[a]
+            } else {
+                // 不选和选硬币 i 这两种方案之和
+                dp[a] = dp[a] + dp[a - coins[i - 1]]
+            }
+        }
+    }
+    return dp[amt]
+}
+
+/* Driver Code */
+fun main() {
+    val coins = intArrayOf(1, 2, 5)
+    val amt = 5
+
+    // 动态规划
+    var res = coinChangeIIDP(coins, amt)
+    println("凑出目标金额的硬币组合数量为 $res")
+
+    // 空间优化后的动态规划
+    res = coinChangeIIDPComp(coins, amt)
+    println("凑出目标金额的硬币组合数量为 $res")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/edit_distance.kt b/codes/kotlin/chapter_dynamic_programming/edit_distance.kt
new file mode 100644
index 000000000..eb80a49a8
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/edit_distance.kt
@@ -0,0 +1,147 @@
+/**
+ * File: edit_distance.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+import java.util.*
+import kotlin.math.min
+
+/* 编辑距离：暴力搜索 */
+fun editDistanceDFS(
+    s: String,
+    t: String,
+    i: Int,
+    j: Int
+): Int {
+    // 若 s 和 t 都为空，则返回 0
+    if (i == 0 && j == 0) return 0
+    // 若 s 为空，则返回 t 长度
+    if (i == 0) return j
+    // 若 t 为空，则返回 s 长度
+    if (j == 0) return i
+    // 若两字符相等，则直接跳过此两字符
+    if (s[i - 1] == t[j - 1]) return editDistanceDFS(s, t, i - 1, j - 1)
+    // 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+    val insert = editDistanceDFS(s, t, i, j - 1)
+    val delete = editDistanceDFS(s, t, i - 1, j)
+    val replace = editDistanceDFS(s, t, i - 1, j - 1)
+    // 返回最少编辑步数
+    return (min(min(insert.toDouble(), delete.toDouble()), replace.toDouble()) + 1).toInt()
+}
+
+/* 编辑距离：记忆化搜索 */
+fun editDistanceDFSMem(
+    s: String,
+    t: String,
+    mem: Array<IntArray>,
+    i: Int,
+    j: Int
+): Int {
+    // 若 s 和 t 都为空，则返回 0
+    if (i == 0 && j == 0) return 0
+    // 若 s 为空，则返回 t 长度
+    if (i == 0) return j
+    // 若 t 为空，则返回 s 长度
+    if (j == 0) return i
+    // 若已有记录，则直接返回之
+    if (mem[i][j] != -1) return mem[i][j]
+    // 若两字符相等，则直接跳过此两字符
+    if (s[i - 1] == t[j - 1]) return editDistanceDFSMem(s, t, mem, i - 1, j - 1)
+    // 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+    val insert = editDistanceDFSMem(s, t, mem, i, j - 1)
+    val delete = editDistanceDFSMem(s, t, mem, i - 1, j)
+    val replace = editDistanceDFSMem(s, t, mem, i - 1, j - 1)
+    // 记录并返回最少编辑步数
+    mem[i][j] = (min(min(insert.toDouble(), delete.toDouble()), replace.toDouble()) + 1).toInt()
+    return mem[i][j]
+}
+
+/* 编辑距离：动态规划 */
+fun editDistanceDP(s: String, t: String): Int {
+    val n = s.length
+    val m = t.length
+    val dp = Array(n + 1) { IntArray(m + 1) }
+    // 状态转移：首行首列
+    for (i in 1..n) {
+        dp[i][0] = i
+    }
+    for (j in 1..m) {
+        dp[0][j] = j
+    }
+    // 状态转移：其余行和列
+    for (i in 1..n) {
+        for (j in 1..m) {
+            if (s[i - 1] == t[j - 1]) {
+                // 若两字符相等，则直接跳过此两字符
+                dp[i][j] = dp[i - 1][j - 1]
+            } else {
+                // 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+                dp[i][j] =
+                    (min(
+                        min(dp[i][j - 1].toDouble(), dp[i - 1][j].toDouble()),
+                        dp[i - 1][j - 1].toDouble()
+                    ) + 1).toInt()
+            }
+        }
+    }
+    return dp[n][m]
+}
+
+/* 编辑距离：空间优化后的动态规划 */
+fun editDistanceDPComp(s: String, t: String): Int {
+    val n = s.length
+    val m = t.length
+    val dp = IntArray(m + 1)
+    // 状态转移：首行
+    for (j in 1..m) {
+        dp[j] = j
+    }
+    // 状态转移：其余行
+    for (i in 1..n) {
+        // 状态转移：首列
+        var leftup = dp[0] // 暂存 dp[i-1, j-1]
+        dp[0] = i
+        // 状态转移：其余列
+        for (j in 1..m) {
+            val temp = dp[j]
+            if (s[i - 1] == t[j - 1]) {
+                // 若两字符相等，则直接跳过此两字符
+                dp[j] = leftup
+            } else {
+                // 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+                dp[j] = (min(min(dp[j - 1].toDouble(), dp[j].toDouble()), leftup.toDouble()) + 1).toInt()
+            }
+            leftup = temp // 更新为下一轮的 dp[i-1, j-1]
+        }
+    }
+    return dp[m]
+}
+
+/* Driver Code */
+fun main() {
+    val s = "bag"
+    val t = "pack"
+    val n = s.length
+    val m = t.length
+
+    // 暴力搜索
+    var res = editDistanceDFS(s, t, n, m)
+    println("将 $s 更改为 $t 最少需要编辑 $res 步")
+
+    // 记忆化搜索
+    val mem = Array(n + 1) { IntArray(m + 1) }
+    for (row in mem) Arrays.fill(row, -1)
+    res = editDistanceDFSMem(s, t, mem, n, m)
+    println("将 $s 更改为 $t 最少需要编辑 $res 步")
+
+    // 动态规划
+    res = editDistanceDP(s, t)
+    println("将 $s 更改为 $t 最少需要编辑 $res 步")
+
+    // 空间优化后的动态规划
+    res = editDistanceDPComp(s, t)
+    println("将 $s 更改为 $t 最少需要编辑 $res 步")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/knapsack.kt b/codes/kotlin/chapter_dynamic_programming/knapsack.kt
new file mode 100644
index 000000000..d1ef7f771
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/knapsack.kt
@@ -0,0 +1,136 @@
+/**
+ * File: knapsack.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+import java.util.*
+import kotlin.math.max
+
+/* 0-1 背包：暴力搜索 */
+fun knapsackDFS(
+    wgt: IntArray,
+    value: IntArray,
+    i: Int,
+    c: Int
+): Int {
+    // 若已选完所有物品或背包无剩余容量，则返回价值 0
+    if (i == 0 || c == 0) {
+        return 0
+    }
+    // 若超过背包容量，则只能选择不放入背包
+    if (wgt[i - 1] > c) {
+        return knapsackDFS(wgt, value, i - 1, c)
+    }
+    // 计算不放入和放入物品 i 的最大价值
+    val no = knapsackDFS(wgt, value, i - 1, c)
+    val yes = knapsackDFS(wgt, value, i - 1, c - wgt[i - 1]) + value[i - 1]
+    // 返回两种方案中价值更大的那一个
+    return max(no.toDouble(), yes.toDouble()).toInt()
+}
+
+/* 0-1 背包：记忆化搜索 */
+fun knapsackDFSMem(
+    wgt: IntArray,
+    value: IntArray,
+    mem: Array<IntArray>,
+    i: Int,
+    c: Int
+): Int {
+    // 若已选完所有物品或背包无剩余容量，则返回价值 0
+    if (i == 0 || c == 0) {
+        return 0
+    }
+    // 若已有记录，则直接返回
+    if (mem[i][c] != -1) {
+        return mem[i][c]
+    }
+    // 若超过背包容量，则只能选择不放入背包
+    if (wgt[i - 1] > c) {
+        return knapsackDFSMem(wgt, value, mem, i - 1, c)
+    }
+    // 计算不放入和放入物品 i 的最大价值
+    val no = knapsackDFSMem(wgt, value, mem, i - 1, c)
+    val yes = knapsackDFSMem(wgt, value, mem, i - 1, c - wgt[i - 1]) + value[i - 1]
+    // 记录并返回两种方案中价值更大的那一个
+    mem[i][c] = max(no.toDouble(), yes.toDouble()).toInt()
+    return mem[i][c]
+}
+
+/* 0-1 背包：动态规划 */
+fun knapsackDP(
+    wgt: IntArray,
+    value: IntArray,
+    cap: Int
+): Int {
+    val n = wgt.size
+    // 初始化 dp 表
+    val dp = Array(n + 1) { IntArray(cap + 1) }
+    // 状态转移
+    for (i in 1..n) {
+        for (c in 1..cap) {
+            if (wgt[i - 1] > c) {
+                // 若超过背包容量，则不选物品 i
+                dp[i][c] = dp[i - 1][c]
+            } else {
+                // 不选和选物品 i 这两种方案的较大值
+                dp[i][c] = max(dp[i - 1][c].toDouble(), (dp[i - 1][c - wgt[i - 1]] + value[i - 1]).toDouble())
+                    .toInt()
+            }
+        }
+    }
+    return dp[n][cap]
+}
+
+/* 0-1 背包：空间优化后的动态规划 */
+fun knapsackDPComp(
+    wgt: IntArray,
+    value: IntArray,
+    cap: Int
+): Int {
+    val n = wgt.size
+    // 初始化 dp 表
+    val dp = IntArray(cap + 1)
+    // 状态转移
+    for (i in 1..n) {
+        // 倒序遍历
+        for (c in cap downTo 1) {
+            if (wgt[i - 1] <= c) {
+                // 不选和选物品 i 这两种方案的较大值
+                dp[c] =
+                    max(dp[c].toDouble(), (dp[c - wgt[i - 1]] + value[i - 1]).toDouble()).toInt()
+            }
+        }
+    }
+    return dp[cap]
+}
+
+/* Driver Code */
+fun main() {
+    val wgt = intArrayOf(10, 20, 30, 40, 50)
+    val value = intArrayOf(50, 120, 150, 210, 240)
+    val cap = 50
+    val n = wgt.size
+
+    // 暴力搜索
+    var res = knapsackDFS(wgt, value, n, cap)
+    println("不超过背包容量的最大物品价值为 $res")
+
+    // 记忆化搜索
+    val mem = Array(n + 1) { IntArray(cap + 1) }
+    for (row in mem) {
+        Arrays.fill(row, -1)
+    }
+    res = knapsackDFSMem(wgt, value, mem, n, cap)
+    println("不超过背包容量的最大物品价值为 $res")
+
+    // 动态规划
+    res = knapsackDP(wgt, value, cap)
+    println("不超过背包容量的最大物品价值为 $res")
+
+    // 空间优化后的动态规划
+    res = knapsackDPComp(wgt, value, cap)
+    println("不超过背包容量的最大物品价值为 $res")
+}
diff --git a/codes/kotlin/chapter_dynamic_programming/min_cost_climbing_stairs_dp.kt b/codes/kotlin/chapter_dynamic_programming/min_cost_climbing_stairs_dp.kt
new file mode 100644
index 000000000..74df32584
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/min_cost_climbing_stairs_dp.kt
@@ -0,0 +1,51 @@
+/**
+ * File: min_cost_climbing_stairs_dp.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+import kotlin.math.min
+
+/* 爬楼梯最小代价：动态规划 */
+fun minCostClimbingStairsDP(cost: IntArray): Int {
+    val n = cost.size - 1
+    if (n == 1 || n == 2) return cost[n]
+    // 初始化 dp 表，用于存储子问题的解
+    val dp = IntArray(n + 1)
+    // 初始状态：预设最小子问题的解
+    dp[1] = cost[1]
+    dp[2] = cost[2]
+    // 状态转移：从较小子问题逐步求解较大子问题
+    for (i in 3..n) {
+        dp[i] = (min(dp[i - 1].toDouble(), dp[i - 2].toDouble()) + cost[i]).toInt()
+    }
+    return dp[n]
+}
+
+/* 爬楼梯最小代价：空间优化后的动态规划 */
+fun minCostClimbingStairsDPComp(cost: IntArray): Int {
+    val n = cost.size - 1
+    if (n == 1 || n == 2) return cost[n]
+    var a = cost[1]
+    var b = cost[2]
+    for (i in 3..n) {
+        val tmp = b
+        b = (min(a.toDouble(), tmp.toDouble()) + cost[i]).toInt()
+        a = tmp
+    }
+    return b
+}
+
+/* Driver Code */
+fun main() {
+    val cost = intArrayOf(0, 1, 10, 1, 1, 1, 10, 1, 1, 10, 1)
+    println("输入楼梯的代价列表为 ${cost.contentToString()}")
+
+    var res = minCostClimbingStairsDP(cost)
+    println("爬完楼梯的最低代价为 $res")
+
+    res = minCostClimbingStairsDPComp(cost)
+    println("爬完楼梯的最低代价为 $res")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/min_path_sum.kt b/codes/kotlin/chapter_dynamic_programming/min_path_sum.kt
new file mode 100644
index 000000000..1d86072d1
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/min_path_sum.kt
@@ -0,0 +1,138 @@
+/**
+ * File: min_path_sum.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+import java.util.*
+import kotlin.math.min
+
+/* 最小路径和：暴力搜索 */
+fun minPathSumDFS(
+    grid: Array<Array<Int>>,
+    i: Int,
+    j: Int
+): Int {
+    // 若为左上角单元格，则终止搜索
+    if (i == 0 && j == 0) {
+        return grid[0][0]
+    }
+    // 若行列索引越界，则返回 +∞ 代价
+    if (i < 0 || j < 0) {
+        return Int.MAX_VALUE
+    }
+    // 计算从左上角到 (i-1, j) 和 (i, j-1) 的最小路径代价
+    val up = minPathSumDFS(grid, i - 1, j)
+    val left = minPathSumDFS(grid, i, j - 1)
+    // 返回从左上角到 (i, j) 的最小路径代价
+    return (min(left.toDouble(), up.toDouble()) + grid[i][j]).toInt()
+}
+
+/* 最小路径和：记忆化搜索 */
+fun minPathSumDFSMem(
+    grid: Array<Array<Int>>,
+    mem: Array<Array<Int>>,
+    i: Int,
+    j: Int
+): Int {
+    // 若为左上角单元格，则终止搜索
+    if (i == 0 && j == 0) {
+        return grid[0][0]
+    }
+    // 若行列索引越界，则返回 +∞ 代价
+    if (i < 0 || j < 0) {
+        return Int.MAX_VALUE
+    }
+    // 若已有记录，则直接返回
+    if (mem[i][j] != -1) {
+        return mem[i][j]
+    }
+    // 左边和上边单元格的最小路径代价
+    val up = minPathSumDFSMem(grid, mem, i - 1, j)
+    val left = minPathSumDFSMem(grid, mem, i, j - 1)
+    // 记录并返回左上角到 (i, j) 的最小路径代价
+    mem[i][j] = (min(left.toDouble(), up.toDouble()) + grid[i][j]).toInt()
+    return mem[i][j]
+}
+
+/* 最小路径和：动态规划 */
+fun minPathSumDP(grid: Array<Array<Int>>): Int {
+    val n = grid.size
+    val m = grid[0].size
+    // 初始化 dp 表
+    val dp = Array(n) { IntArray(m) }
+    dp[0][0] = grid[0][0]
+    // 状态转移：首行
+    for (j in 1..<m) {
+        dp[0][j] = dp[0][j - 1] + grid[0][j]
+    }
+    // 状态转移：首列
+    for (i in 1..<n) {
+        dp[i][0] = dp[i - 1][0] + grid[i][0]
+    }
+    // 状态转移：其余行和列
+    for (i in 1..<n) {
+        for (j in 1..<m) {
+            dp[i][j] =
+                (min(dp[i][j - 1].toDouble(), dp[i - 1][j].toDouble()) + grid[i][j]).toInt()
+        }
+    }
+    return dp[n - 1][m - 1]
+}
+
+/* 最小路径和：空间优化后的动态规划 */
+fun minPathSumDPComp(grid: Array<Array<Int>>): Int {
+    val n = grid.size
+    val m = grid[0].size
+    // 初始化 dp 表
+    val dp = IntArray(m)
+    // 状态转移：首行
+    dp[0] = grid[0][0]
+    for (j in 1..<m) {
+        dp[j] = dp[j - 1] + grid[0][j]
+    }
+    // 状态转移：其余行
+    for (i in 1..<n) {
+        // 状态转移：首列
+        dp[0] = dp[0] + grid[i][0]
+        // 状态转移：其余列
+        for (j in 1..<m) {
+            dp[j] = (min(dp[j - 1].toDouble(), dp[j].toDouble()) + grid[i][j]).toInt()
+        }
+    }
+    return dp[m - 1]
+}
+
+/* Driver Code */
+fun main() {
+    val grid = arrayOf(
+        arrayOf(1, 3, 1, 5),
+        arrayOf(2, 2, 4, 2),
+        arrayOf(5, 3, 2, 1),
+        arrayOf(4, 3, 5, 2)
+    )
+    val n = grid.size
+    val m = grid[0].size
+
+    // 暴力搜索
+    var res = minPathSumDFS(grid, n - 1, m - 1)
+    println("从左上角到右下角的最小路径和为 $res")
+
+    // 记忆化搜索
+    val mem = Array(n) { Array(m) { 0 } }
+    for (row in mem) {
+        Arrays.fill(row, -1)
+    }
+    res = minPathSumDFSMem(grid, mem, n - 1, m - 1)
+    println("从左上角到右下角的最小路径和为 $res")
+
+    // 动态规划
+    res = minPathSumDP(grid)
+    println("从左上角到右下角的最小路径和为 $res")
+
+    // 空间优化后的动态规划
+    res = minPathSumDPComp(grid)
+    println("从左上角到右下角的最小路径和为 $res")
+}
\ No newline at end of file
diff --git a/codes/kotlin/chapter_dynamic_programming/unbounded_knapsack.kt b/codes/kotlin/chapter_dynamic_programming/unbounded_knapsack.kt
new file mode 100644
index 000000000..798726195
--- /dev/null
+++ b/codes/kotlin/chapter_dynamic_programming/unbounded_knapsack.kt
@@ -0,0 +1,74 @@
+/**
+ * File: unbounded_knapsack.kt
+ * Created Time: 2024-01-25
+ * Author: curtishd (1023632660@qq.com)
+ */
+
+package chapter_dynamic_programming
+
+import kotlin.math.max
+
+/* 完全背包：动态规划 */
+fun unboundedKnapsackDP(
+    wgt: IntArray,
+    value: IntArray,
+    cap: Int
+): Int {
+    val n = wgt.size
+    // 初始化 dp 表
+    val dp = Array(n + 1) { IntArray(cap + 1) }
+    // 状态转移
+    for (i in 1..n) {
+        for (c in 1..cap) {
+            if (wgt[i - 1] > c) {
+                // 若超过背包容量，则不选物品 i
+                dp[i][c] = dp[i - 1][c]
+            } else {
+                // 不选和选物品 i 这两种方案的较大值
+                dp[i][c] = max(dp[i - 1][c].toDouble(), (dp[i][c - wgt[i - 1]] + value[i - 1]).toDouble())
+                    .toInt()
+            }
+        }
+    }
+    return dp[n][cap]
+}
+
+/* 完全背包：空间优化后的动态规划 */
+fun unboundedKnapsackDPComp(
+    wgt: IntArray,
+    value: IntArray,
+    cap: Int
+): Int {
+    val n = wgt.size
+    // 初始化 dp 表
+    val dp = IntArray(cap + 1)
+    // 状态转移
+    for (i in 1..n) {
+        for (c in 1..cap) {
+            if (wgt[i - 1] > c) {
+                // 若超过背包容量，则不选物品 i
+                dp[c] = dp[c]
+            } else {
+                // 不选和选物品 i 这两种方案的较大值
+                dp[c] =
+                    max(dp[c].toDouble(), (dp[c - wgt[i - 1]] + value[i - 1]).toDouble()).toInt()
+            }
+        }
+    }
+    return dp[cap]
+}
+
+/* Driver Code */
+fun main() {
+    val wgt = intArrayOf(1, 2, 3)
+    val value = intArrayOf(5, 11, 15)
+    val cap = 4
+
+    // 动态规划
+    var res = unboundedKnapsackDP(wgt, value, cap)
+    println("不超过背包容量的最大物品价值为 $res")
+
+    // 空间优化后的动态规划
+    res = unboundedKnapsackDPComp(wgt, value, cap)
+    println("不超过背包容量的最大物品价值为 $res")
+}
\ No newline at end of file