Description of the Lab
Overview of the Lab
In this Lab, we increase the difficulty once more by introducing multiple pieces of cheese in the maze.
The goal of the Lab is to program an exhaustive search of possible paths that go through all pieces of cheese, in order to find the solution to the traveling salesman problem. Then, we improve this exhaustive search using an optimization called backtracking.
Set up the environment
As always, you should start by duplicating the PyRat template file on Google Colab. For recall, you should go on the Google Colab template program, click on « File », then « Save copy in drive ». Rename it as
exhaustive.ipynb, and make it accessible by link. We will refer to this link as
In this Lab, we will use the following command:
python pyrat.py --rat <shared_link_to_exhaustive.ipynb> -p 5 -x 15 -y 15 -d 0.5 --random_seed 1
It is now time to increase the number of pieces of cheese in the maze (
-p option). Keep this number low in a first time, as the program we are going to write is quite complex.
The traveling salesman problem
Generating the meta-graph
As studied in the Episode, the traveling salesman problem requires a complete graph to work. We are going to build such a graph, that abstracts some locations in the maze (we call it the meta-graph).
First, you should write a function that returns a complete graph, in which vertices represent the locations of the pieces of cheese and the starting position, and of which edges are weighted by the lengths of the shortest paths connecting these vertices.
Note: You should also store these shortest paths somewhere in order to move in the maze from a vertex of the meta-graph to another one.
def build_meta_graph (maze_map, locations) : # Return the meta-graph and all necessary routing tables
An easy way to perform an exhaustive search is to see it as a depth-first search, in which we want each vertex to be visited within each branch, rather than only once in the whole search. Here is an example of a recursive depth-first search algorithm:
def dfs (graph, initial_vertex) : # Nonlocal list of visited vertices (nonlocal variables are basically global variables for nested functions) visited_vertices =  # Recursive implementation of a depth-first search def _dfs (current_vertex, current_length) : # Visiting a vertex print("Visiting", current_vertex, "at distance", current_length, "from start") # We stop when all vertices are visited nonlocal visited_vertices if len(visited_vertices) == len(graph) : print("Back") return # If there are still vertices to visit, we explore unexplored neighbors for neighbor in graph[current_vertex] : if neighbor not in visited_vertices : visited_vertices.append(neighbor) _dfs(neighbor, current_length + graph[current_vertex][neighbor]) # If there are no unvisited neighbors left, the inner function will return, # which corresponds to coming back to the previously visited vertex print("Back") # Initial call _dfs(initial_vertex, 0)
Now, adapt the DFS code above to obtain an exhaustive search.
def tsp (graph, initial_vertex) : def _tsp (...) : # Recursive implementation of the tree exploration # Initial call return _tsp(...)
Link the path in the meta-graph and the maze
Once the result of the traveling salesman problem is obtained, write a function to transform the solution path (into the meta-graph) into a sequence of locations within the maze.
def meta_graph_route_to_route (meta_graph_route, routing_tables) : # Return the sequence of locations in the maze to perform a route in the meta-graph
Then, use the code you designed in previous labs to transform this list into a list of moves to apply, and get all pieces of cheese in a minimum number of moves.
Implement a backtracking strategy
At this point, you should have a program which checks all possible permutations of vertices in the meta-graph, finds the one leading to the shortest path, and then converts this permutation into moves.
Now, you should improve this by implementing a backtracking strategy, that will abort exploration of a branch if it is already longer than the current best.
It would be interesting to work on a different file now, and create a program called
exhaustive_backtracking.ipynb. This would allow you to compare performance of the exhaustive search with or without this optimization.