Bubble Sort
Selection Sort
Insertion Sort
Merge Sort
Quick Sort
Heap Sort
Linear Search
Binary Search
Jump Search
BST Insert
BST Search
In-order Traversal

Breadth-First Search
Depth-First Search
Dijkstra's Shortest Path
Prim's MST
Kruskal's MST
Topological Sort (Kahn's)
Tower of Hanoi
N-Queens
Permutations (Backtracking)
Fibonacci (DP)
0/1 Knapsack
Longest Common Subsequence
Coin Change (Min Coins)

Hill Climbing
Simulated Annealing
Differential Evolution

Q-Learning

Genetic Algorithm
Particle Swarm Optimization
Ant Colony Optimization

Monte Carlo Simulation

Placeholder — future integration.

"The invisible architecture through which intelligence becomes impact."

04Learning·Reinforcement Learning

Q-Learning

⏱ O(episodes·steps)◧ O(|S|·|A|)



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Step 1 / 14

Speed

Speed

04Learning

Q-Learning

Reinforcement Learning

Bootstrap value estimates from delayed reward.

Time

O(episodes·steps)

Space

O(|S|·|A|)

Applications

Robotic control
Game-playing agents
Adaptive systems

Reasoning Trace

Q-Learning on 4×4 grid. Goal ★ = +1, trap × = −1. α=0.4, γ=0.9, ε=0.6. Agent learns by trial-and-error.

episode0

epsilon0.60

Principle in Pseudocode

1Q(s,a) ← 0
2for each episode:
3 s ← start
4 while not terminal:
5 a ← ε-greedy(Q, s)
6 observe r, s'
7 Q(s,a) ← Q(s,a) + α[r + γ·max Q(s',·) − Q(s,a)]
8 s ← s'

Reference implementation · pseudo

// Tabular Q-Learning
// See pseudocode →