misc changes

astar2.py

@@ -19,10 +19,6 @@ class PlanRoute():
         self.puddles = puddles
 
     def actions(self, state):
-        """ Return the actions that can be executed in the given state.
-        The result would be a list, since there are only three possible actions
-        in any given state of the environment """
-
         possible_actions = ['Forward', 'Left', 'Right']
         x, y = state.get_location()
         orientation = state.get_orientation()
@@ -104,12 +100,6 @@ class PlanRoute():
         return state.get_location() == self.goal.get_location()
 
     def path_cost(self, c, state1, action, state2):
-        """Return the cost of a solution path that arrives at state2 from
-        state1 via action, assuming cost c to get up to state1. If the problem
-        is such that the path doesn't matter, this function will only look at
-        state2. If the path does matter, it will consider c and maybe state1
-        and action. The default method costs 1 for every step in the path."""
-
         if action == "Forward" or action == "Left" or action == "Right":
 
             
@@ -430,13 +420,6 @@ class SweeperAgent:
     @staticmethod
     #def best_first_graph_search(problem, f, display=False):
     def best_first_graph_search(problem, f, display=True):
-        """Search the nodes with the lowest f scores first.
-        You specify the function f(node) that you want to minimize; for example,
-        if f is a heuristic estimate to the goal, then we have greedy best
-        first search; if f is node.depth then we have breadth-first search.
-        There is a subtlety: the line "f = memoize(f, 'f')" means that the f
-        values will be cached on the nodes as they are computed. So after doing
-        a best first search you can examine the f values of the path returned."""
         #f = memoize(f, 'f')
 
         """TODO"""