Dec 19, 2025

Intermediate 25 min

🎉 Congratulations!

You’ve completed the Interactive Pathfinding: Dijkstra vs A on a Grid* tutorial!

What You Learned

By completing this tutorial, you now understand:

✅ Grid to Graph Mapping - How 2D grids become graph structures
✅ Dijkstra’s Algorithm - Finding optimal paths by exploring outward
✅ A Search* - Using heuristics to focus search toward the goal
✅ Algorithm Comparison - When to use each algorithm
✅ Implementation - How to code both algorithms from scratch
✅ Practical Application - Building your own pathfinding tools

Key Concepts Recap

Grids as Graphs

Grid cells = nodes
Movement between cells = edges
Movement costs = edge weights
Obstacles = no edges

Dijkstra’s Algorithm

Explores all directions equally
Uses priority queue with distance from start
Guarantees optimal paths (with non-negative weights)
Good for multiple goals or unknown goal locations

A* Algorithm

Uses heuristic to guide search
Priority = g(n) + h(n) instead of just g(n)
Usually explores fewer nodes than Dijkstra
Still finds optimal paths (with admissible heuristic)

When to Use What

Dijkstra: Multiple goals, unknown goal, no good heuristic
A:* Single known goal, good heuristic available, performance matters

Next Steps

Continue Learning

Advanced Pathfinding Algorithms:

Bellman-Ford - Handles negative weights
Floyd-Warshall - All-pairs shortest paths
Jump Point Search - Optimization for uniform grids
Theta* - Any-angle pathfinding

Related Topics:

Graph algorithms - BFS, DFS, topological sort
Dynamic programming - For pathfinding with constraints
Game AI - Using pathfinding in game development
Robotics - Real-world pathfinding applications

Practice Projects

Maze Solver - Generate random mazes and solve them
Game AI - Add pathfinding to a simple game
Route Optimizer - Find optimal routes on real maps
Multi-agent Pathfinding - Handle multiple units avoiding each other

Resources

Books:

“Introduction to Algorithms” (CLRS) - Chapter on shortest paths
“Artificial Intelligence: A Modern Approach” - Search algorithms chapter

Online:

Red Blob Games - Excellent interactive pathfinding tutorials
Wikipedia - Detailed algorithm descriptions
GitHub - Open-source pathfinding implementations

Final Quiz

Test your understanding one more time:

Knowledge Check

This interactive quiz requires JavaScript to be enabled.

Question 1: What is the main difference between Dijkstra and A*?

A. A* is always faster
B. A* uses a heuristic to guide the search (Correct)
C. Dijkstra doesn't find optimal paths
D. A* only works on grids

Explanation: A* uses a heuristic function h(n) to estimate remaining cost to the goal, which guides the search toward the goal. Dijkstra explores all directions equally.

Question 2: What makes a heuristic 'admissible'?

A. It's fast to compute
B. It never overestimates the true cost (Correct)
C. It's always accurate
D. It's easy to implement

Explanation: An admissible heuristic never overestimates the true cost from a node to the goal. This property ensures A* finds optimal paths.

Question 3: When should you prefer Dijkstra over A*?

A. When you have a single known goal
B. When you need paths to multiple destinations (Correct)
C. When you have a good heuristic
D. When performance is critical

Explanation: Dijkstra naturally finds paths to all nodes from a source, making it ideal when you need multiple destinations. A* would require running separately for each goal.

Question 4: What data structure is essential for both Dijkstra and A*?

A. Stack
B. Queue
C. Priority Queue (Min-Heap) (Correct)
D. Hash Table

Explanation: Both algorithms use a priority queue (min-heap) to always process the node with the smallest priority value (distance for Dijkstra, f-score for A*).

You’ve learned fundamental pathfinding algorithms that power navigation apps, games, and robotics. Share your progress:

Build a pathfinding project and share it
Explain the concepts to someone else (teaching helps learning)
Contribute to open-source pathfinding libraries
Apply these algorithms to real problems

Thank You!

Thanks for completing this tutorial. Pathfinding is a fundamental skill in computer science, and you now have a solid understanding of two of the most important algorithms.

Keep practicing, keep building, and keep learning!

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