diff options
| -rw-r--r-- | .vscode/launch.json | 30 | ||||
| -rw-r--r-- | searchAgents.py | 50 |
2 files changed, 74 insertions, 6 deletions
diff --git a/.vscode/launch.json b/.vscode/launch.json index 4788109..af599a5 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -79,7 +79,7 @@ "--frameTime=0.02", "--pacman=SearchAgent", "--agentArgs=fn=uniformCostSearch,prob=CornersProblem", - "--layout=${input:corners_mazeLayout}", + "--layout=${input:cornersLayout}", ] }, { @@ -90,7 +90,19 @@ "args": [ "--frameTime=0.02", "--pacman=AStarCornersAgent", - "--layout=${input:corners_mazeLayout}", + "--layout=${input:cornersLayout}", + ] + }, + { + "name": "USCFoodSearchAgent", + "type": "python", + "request": "launch", + "program": "pacman.py", + "args": [ + "--frameTime=0.02", + "--pacman=SearchAgent", + "--agentArgs=fn=uniformCostSearch,prob=FoodSearchProblem", + "--layout=${input:foodLayout}", ] }, { @@ -101,7 +113,7 @@ "args": [ "--frameTime=0.02", "--pacman=AStarFoodSearchAgent", - "--layout=${input:corners_mazeLayout}", + "--layout=${input:foodLayout}", ] } ], @@ -129,7 +141,7 @@ "default": "nullHeuristic" }, { - "id": "corners_mazeLayout", + "id": "cornersLayout", "type": "pickString", "description": "Select the maze layout", "options": [ @@ -138,6 +150,16 @@ "bigCorners" ], "default": "tinyCorners" + }, + { + "id": "foodLayout", + "type": "pickString", + "description": "Select the maze layout", + "options": [ + "testSearch", + "trickySearch" + ], + "default": "tinyCorners" } ] }
\ No newline at end of file diff --git a/searchAgents.py b/searchAgents.py index 9a6f0ab..9305c14 100644 --- a/searchAgents.py +++ b/searchAgents.py @@ -482,8 +482,54 @@ def foodHeuristic(state, problem): problem.heuristicInfo['wallCount'] """ position, foodGrid = state - "*** YOUR CODE HERE ***" - return 0 + wallGrid = problem.walls + + def getDistance(food): + distance = abs(food[0] - position[0]) + abs(food[1] - position[1]) + + if position[0] == food[0] or position[1] == food[1]: + return distance + + # if position[0] == food[0]: + # lowerBound = min((position[1], food[1])) + # upperBound = max((position[1], food[1])) + # wallsBetween = wallGrid[position[0]][lowerBound:upperBound] + # if any(wallsBetween): + # return distance + 2 + # return distance + + # if position[1] == food[1]: + # lowerBound = min((position[0], food[0])) + # upperBound = max((position[0], food[0])) + # wallsBetween = map(lambda yVals: yVals[position[1]], wallGrid[lowerBound:upperBound]) + # if any(wallsBetween): + # return distance + 2 + # return distance + + xOffset = (food[0] - position[0]) / abs(food[0] - position[0]) + yOffset = (food[1] - position[1]) / abs(food[1] - position[1]) + + if wallGrid[position[0] + xOffset][position[1]] and wallGrid[position[0]][position[1] + yOffset]: + distance += 2 + + if wallGrid[food[0] + (xOffset * -1) ][food[1]] and wallGrid[food[0]][food[1] + (yOffset * -1)]: + distance += 2 + + return distance + + foodList = foodGrid.asList() + foodLeft = len(foodList) + + if foodLeft == 0: + return 0 + + foodDist = map(getDistance, foodList) + + maxFood = max(foodDist) + minFood = min(foodDist) + + return max((maxFood, minFood + foodLeft - 1)) + class ClosestDotSearchAgent(SearchAgent): "Search for all food using a sequence of searches" |