refactor: fix some problem. Complete p. 5
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165
search.py
165
search.py
@ -82,109 +82,97 @@ def depthFirstSearch(problem: SearchProblem):
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"""
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"*** YOUR CODE HERE ***"
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actions = []
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path = []
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curr_state = problem.getStartState()
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curr_state = problem.getStartState() # (5, 5)
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isGoal = problem.isGoalState(curr_state)
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visited = list([curr_state])
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visited = list()
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stack = util.Stack()
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stack.push(((curr_state, 'None', 0), path)) # (state, path)
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while not isGoal:
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# push in the Stack
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successors = problem.getSuccessors(curr_state)
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for (state, direction, cost) in successors:
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if state not in visited:
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stack.push({
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'state': (state, direction, cost),
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'actions': actions
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})
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# check whether reachable
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if stack.isEmpty():
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return []
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else:
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obj = stack.pop()
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(curr_state, direction, cost) = obj['state']
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visited.append(curr_state)
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actions = obj['actions'] + [ direction ]
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isGoal = problem.isGoalState(curr_state)
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((new_state, direction, cost), path) = stack.pop() # ((location, direction, cost), path)
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if new_state in visited:
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continue
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print(new_state, path)
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path = path + [direction]
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visited.append(new_state)
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isGoal = problem.isGoalState(new_state)
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for (_state, _direction, _cost) in problem.getSuccessors(new_state): # (state, direction, cost)
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stack.push(((_state, _direction, _cost), path))
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print(path)
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path = path[1:]
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return actions
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return path
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def breadthFirstSearch(problem: SearchProblem):
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"""Search the shallowest nodes in the search tree first."""
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"*** YOUR CODE HERE ***"
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actions = []
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path = []
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curr_state = problem.getStartState()
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curr_state = problem.getStartState() # (5, 5)
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isGoal = problem.isGoalState(curr_state)
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visited = list([curr_state])
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visited = list()
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queue = util.Queue()
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queue.push(((curr_state, 'None', 0), path)) # (state, path)
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while not isGoal:
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print(curr_state)
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# push in the Stack
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successors = problem.getSuccessors(curr_state)
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for (state, direction, cost) in successors:
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if state not in visited:
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visited.append(state)
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queue.push({
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'state': (state, direction, cost),
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'actions': actions
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})
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# check whether reachable
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if queue.isEmpty():
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return []
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else:
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obj = queue.pop()
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(curr_state, direction, cost) = obj['state']
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actions = obj['actions'] + [ direction ]
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isGoal = problem.isGoalState(curr_state)
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((new_state, direction, cost), path) = queue.pop() # ((location, direction, cost), path)
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if new_state in visited:
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continue
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print(new_state, path)
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path = path + [direction]
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visited.append(new_state)
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isGoal = problem.isGoalState(new_state)
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for (_state, _direction, _cost) in problem.getSuccessors(new_state): # (state, direction, cost)
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queue.push(((_state, _direction, _cost), path))
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print(path)
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path = path[1:]
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return actions
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return path
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def uniformCostSearch(problem: SearchProblem):
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"""Search the node of least total cost first."""
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"*** YOUR CODE HERE ***"
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actions = []
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curr_state = problem.getStartState()
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isGoal = problem.isGoalState(curr_state)
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path = []
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costs = 0
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visited = list([curr_state])
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curr_state = problem.getStartState() # (5, 5)
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isGoal = problem.isGoalState(curr_state)
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visited = list()
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queue = util.PriorityQueue()
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queue.push(((curr_state, 'None', 0), path, costs), 0) # (state, path)
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while not isGoal:
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# push in the P Queue
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successors = problem.getSuccessors(curr_state)
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for (state, direction, cost) in successors:
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if state not in visited:
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queue.push({
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'state': (state, direction, cost),
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'actions': actions,
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'costs': costs
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}, costs + cost) # if visited this successor node, how much cost will the agent need
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# check whether reachable
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if queue.isEmpty():
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return []
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else:
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obj = queue.pop()
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(curr_state, direction, cost) = obj['state']
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visited.append(curr_state)
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actions = obj['actions'] + [ direction ]
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costs = obj['costs'] + cost
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((new_state, direction, cost), path, costs) = queue.pop() # ((location, direction, cost), path)
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if new_state in visited:
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continue
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print(new_state, path)
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path = path + [direction]
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costs = costs + cost
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visited.append(new_state)
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isGoal = problem.isGoalState(new_state)
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for (_state, _direction, _cost) in problem.getSuccessors(new_state): # (state, direction, cost)
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queue.push(((_state, _direction, _cost), path, costs), costs+_cost)
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print(path)
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path = path[1:]
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isGoal = problem.isGoalState(curr_state)
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return actions
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return path
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def nullHeuristic(state, problem=None):
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@ -197,41 +185,40 @@ def nullHeuristic(state, problem=None):
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def aStarSearch(problem: SearchProblem, heuristic=nullHeuristic):
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"""Search the node that has the lowest combined cost and heuristic first."""
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"*** YOUR CODE HERE ***"
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actions = []
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curr_state = problem.getStartState()
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isGoal = problem.isGoalState(curr_state)
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path = []
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costs = 0
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visited = list([curr_state])
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curr_state = problem.getStartState() # (5, 5)
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isGoal = problem.isGoalState(curr_state)
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visited = list()
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queue = util.PriorityQueue()
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queue.push(((curr_state, 'None', 0), path, costs), 0) # (state, path)
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min_cost = {curr_state: 0}
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while not isGoal:
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# push in the P Queue
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successors = problem.getSuccessors(curr_state)
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for (state, direction, cost) in successors:
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if state not in visited:
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queue.push({
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'state': (state, direction, cost),
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'actions': actions,
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'costs': costs
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}, costs + cost + heuristic(state, problem)) # if visited this successor node, how much cost will the agent need
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# check whether reachable
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if queue.isEmpty():
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return []
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else:
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obj = queue.pop()
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(curr_state, direction, cost) = obj['state']
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visited.append(curr_state)
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actions = obj['actions'] + [ direction ]
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costs = obj['costs'] + cost
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isGoal = problem.isGoalState(curr_state)
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return actions
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((new_state, direction, cost), path, costs) = queue.pop() # ((location, direction, cost), path)
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if new_state in visited and (costs+cost)>=min_cost[new_state]:
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continue
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print(new_state, path)
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path = path + [direction]
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costs = costs + cost
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min_cost[new_state] = costs
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visited.append(new_state)
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isGoal = problem.isGoalState(new_state)
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if isGoal:
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break
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for (_state, _direction, _cost) in problem.getSuccessors(new_state): # (state, direction, cost)
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print(f" {_state} -> {_cost}")
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queue.push(((_state, _direction, _cost), path, costs), costs+_cost)
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print(path)
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path = path[1:]
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return path
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# Abbreviations
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@ -365,9 +365,12 @@ def cornersHeuristic(state: Any, problem: CornersProblem):
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"""
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corners = problem.corners # These are the corner coordinates
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walls = problem.walls # These are the walls of the maze, as a Grid (game.py)
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return 0
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"*** YOUR CODE HERE ***"
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return 0 # Default to trivial solution
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x, y = state['position']
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distances = [ util.manhattanDistance((x, y), corner) for corner in corners ]
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return min(distances)
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class AStarCornersAgent(SearchAgent):
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"A SearchAgent for FoodSearchProblem using A* and your foodHeuristic"
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