Search algorithm implementations meant for teaching

Related tags

Algorithms Search-py
Overview

Search-py

A collection of search algorithms for teaching and experimenting.

Non-adversarial Search

There’s a heavy separation of concerns which leads to many tiny classes with a small scope. The library might be a bit overwhelming and seem to be an overkill, but it should allow us to solve many different problems with many algorithms to better understand their behavior.

There’s a generic SearchAlgorithm implementation that can be extended to implement (graph)-DFS, BFS, Dijkstra’s Algorithm, A*. Currently only DFS and BFS are implemented.

Class overview

(root)

search

  • space.py: Space, PredefinedSpace, RandomAccessSpace, Problem
algorithms
  • search.py: Node, SearchAlgorithm, Open
  • dfs.py: DFS(SearchAlgorithm), DFS.Open(SearchAlgorithm.Open)
  • bfs.py: BFS(SearchAlgorithm), BFS.Open(SearchAlgorithm.Open)
  • dijkstra.py: Dijkstra(SearchAlgorithm), Dijkstra.Open(SearchAlgorithm.Open)
problems
grid
  • board2d.py: class Board2D(Space, PredefinedSpace, RandomAccessSpace)
  • main.py: Has a super simple benchmark.
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Comments
  • Allow the action cost to depend on the state

    Allow the action cost to depend on the state

    Actions are currently independent of the States, which makes it impossible to look at the state to determine the cost of an action.

    Agents carrying items would naturally want to consider moving with more weight more expensive.

    enhancement 
    opened by Dietr1ch 0
Owner
Dietrich Daroch
Dietrich Daroch
GitHub
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