How can I implement the A* algorithm in Python?

How can I implement the A* algorithm in Python?

To implement the A* algorithm in Python, you'll need to define a few key components: the graph, the heuristic function, and the open and closed sets.

What data structures should I use for the open and closed sets?

You can use a priority queue for the open set to efficiently get the node with the lowest cost, and a set for the closed set to track visited nodes.

Can you provide a simple code example?

Sure! Here's a basic implementation:

import heapq

def a_star(start, goal, graph, heuristic):
    open_set = []
    heapq.heappush(open_set, (0, start))
    came_from = {}
    g_score = {node: float('inf') for node in graph}
    g_score[start] = 0
    f_score = {node: float('inf') for node in graph}
    f_score[start] = heuristic[start]

    while open_set:
        current = heapq.heappop(open_set)[1]
        if current == goal:
            return reconstruct_path(came_from, current)

        for neighbor in graph[current]:
            tentative_g_score = g_score[current] + graph[current][neighbor]
            if tentative_g_score < g_score[neighbor]:
                came_from[neighbor] = current
                g_score[neighbor] = tentative_g_score
                f_score[neighbor] = g_score[neighbor] + heuristic[neighbor]
                if neighbor not in [i[1] for i in open_set]:
                    heapq.heappush(open_set, (f_score[neighbor], neighbor))
    return False

What are some common heuristics I can use?

Common heuristics include the Manhattan distance for grid-based maps and the Euclidean distance for continuous spaces.