ó ðè9Qc@sœdZddlmZddlmZddlmZddlZddlZddlZdefd„ƒYZdefd „ƒYZ d ej fd „ƒYZ d e fd „ƒYZ de fd„ƒYZ id„Zid„Zdej fd„ƒYZd„Zde fd„ƒYZdd#d„ƒYZde fd„ƒYZd„Zde fd„ƒYZde fd„ƒYZd efd!„ƒYZd"„ZdS($s« This file contains all of the agents that can be selected to control Pacman. To select an agent, use the '-p' option when running pacman.py. Arguments can be passed to your agent using '-a'. For example, to load a SearchAgent that uses depth first search (dfs), run the following command: > python pacman.py -p SearchAgent -a fn=depthFirstSearch Commands to invoke other search strategies can be found in the project description. Please only change the parts of the file you are asked to. Look for the lines that say "*** YOUR CODE HERE ***" The parts you fill in start about 3/4 of the way down. Follow the project description for details. Good luck and happy searching! iÿÿÿÿ(t Directions(tAgent(tActionsNt GoWestAgentcBseZdZd„ZRS(s'An agent that goes West until it can't.cCs'tj|jƒkrtjStjSdS(s6The agent receives a GameState (defined in pacman.py).N(RtWESTtgetLegalPacmanActionstSTOP(tselftstate((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyt getAction+s(t__name__t __module__t__doc__R (((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR(st SearchAgentcBs2eZdZdddd„Zd„Zd„ZRS(s’ This very general search agent finds a path using a supplied search algorithm for a supplied search problem, then returns actions to follow that path. As a default, this agent runs DFS on a PositionSearchProblem to find location (1,1) Options for fn include: depthFirstSearch or dfs breadthFirstSearch or bfs Note: You should NOT change any code in SearchAgent tdepthFirstSearchtPositionSearchProblemt nullHeuristiccs$|ttƒkr"t|d‚ntt|ƒ‰dˆjjkrXd|GHˆ|_nz|tƒjƒkr}tƒ|‰n1|ttƒkr¡tt|ƒ‰n t|d‚d||fGH‡‡fd†|_|tƒjƒks÷|j dƒ rt|d‚ntƒ||_ d |GHdS( Ns' is not a search function in search.py.t heuristics[SearchAgent] using function s3 is not a function in searchAgents.py or search.py.s0[SearchAgent] using function %s and heuristic %scsˆ|dˆƒS(NR((tx(theurtfunc(sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pytYstProblems1 is not a search problem type in SearchAgents.py.s![SearchAgent] using problem type ( tdirtsearchtAttributeErrortgetattrt func_codet co_varnamestsearchFunctiontglobalstkeystendswitht searchType(RtfntprobR((RRsfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyt__init__Fs"   %cCs˜|jdkrtd‚ntjƒ}|j|ƒ}|j|ƒ|_|j|jƒ}d|tjƒ|fGHdt|ƒkr”d|jGHndS(sF This is the first time that the agent sees the layout of the game board. Here, we choose a path to the goal. In this phase, the agent should compute the path to the goal and store it in a local variable. All of the work is done in this method! state: a GameState object (pacman.py) s+No search function provided for SearchAgents0Path found with total cost of %d in %.1f secondst _expandedsSearch nodes expanded: %dN( RtNonet ExceptionttimeR!tactionstgetCostOfActionsRR%(RRt starttimetproblemt totalCost((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pytregisterInitialStateas  cCsadt|ƒkrd|_n|j}|jd7_|t|jƒkrV|j|StjSdS(sÕ Returns the next action in the path chosen earlier (in registerInitialState). Return Directions.STOP if there is no further action to take. state: a GameState object (pacman.py) t actionIndexiiN(RR/tlenR)RR(RRti((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR qs   (R R R R$R.R (((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR 7s  RcBsMeZdZd„dd eed„Zd„Zd„Zd„Zd„Z RS( sc A search problem defines the state space, start state, goal test, successor function and cost function. This search problem can be used to find paths to a particular point on the pacman board. The state space consists of (x,y) positions in a pacman game. Note: this search problem is fully specified; you should NOT change it. cCsdS(Ni((R((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR‹sicCs¢|jƒ|_|jƒ|_|dkr6||_n||_||_||_|r|jƒdksy|j |Œ rdGHnigd|_ |_ |_ dS(sÙ Stores the start and goal. gameState: A GameState object (pacman.py) costFn: A function from a search state (tuple) to a non-negative number goal: A position in the gameState is6Warning: this does not look like a regular search mazeiN( tgetWallstwallstgetPacmanPositiont startStateR&tgoaltcostFnt visualizet getNumFoodthasFoodt_visitedt _visitedlistR%(Rt gameStateR7R6tstarttwarnR8((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR$‹s    (cCs|jS(N(R5(R((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyt getStartStateŸscCs||jk}|r}|jr}|jj|ƒddl}dt|ƒkr}dt|jƒkrz|jj|jƒqzq}n|S(Niÿÿÿÿt_displaytdrawExpandedCells(R6R8R<tappendt__main__RRARB(RRtisGoalRD((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyt isGoalState¢s c Csøg}x­tjtjtjtjgD]}|\}}tj|ƒ\}}t||ƒt||ƒ}} |j|| s%|| f} |j | ƒ} |j | || fƒq%q%W|j d7_ ||j krôt |j |<|jj |ƒn|S(s« Returns successor states, the actions they require, and a cost of 1. As noted in search.py: For a given state, this should return a list of triples, (successor, action, stepCost), where 'successor' is a successor to the current state, 'action' is the action required to get there, and 'stepCost' is the incremental cost of expanding to that successor i(RtNORTHtSOUTHtEASTRRtdirectionToVectortintR3R7RCR%R;tTrueR<( RRt successorstactionRtytdxtdytnextxtnextyt nextStatetcost((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyt getSuccessors¯s % !  cCs¡|dkrdS|jƒ\}}d}xr|D]j}tj|ƒ\}}t||ƒt||ƒ}}|j||r€dS||j||fƒ7}q/W|S(sˆ Returns the cost of a particular sequence of actions. If those actions include an illegal move, return 999999 i?BiN(R&R@RRJRKR3R7(RR)RRORURNRPRQ((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR*Ís  !(iiN( R R R R&RLR$R@RFRVR*(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR€s    tStayEastSearchAgentcBseZdZd„ZRS(sÄ An agent for position search with a cost function that penalizes being in positions on the West side of the board. The cost function for stepping into a position (x,y) is 1/2^x. cs+tj|_d„‰‡fd†|_dS(NcSs d|dS(Ngà?i((tpos((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRæscs t|ˆƒS(N(R(R(R7(sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRçs(RtuniformCostSearchRR!(R((R7sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR$äs  (R R R R$(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRWÝstStayWestSearchAgentcBseZdZd„ZRS(s An agent for position search with a cost function that penalizes being in positions on the East side of the board. The cost function for stepping into a position (x,y) is 2^x. cs+tj|_d„‰‡fd†|_dS(NcSs d|dS(Nii((RX((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRòscs t|ˆƒS(N(R(R(R7(sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRós(RRYRR!(R((R7sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR$ðs  (R R R R$(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRZéscCs;|}|j}t|d|dƒt|d|dƒS(s<The Manhattan distance heuristic for a PositionSearchProblemii(R6tabs(tpositionR,tinfotxy1txy2((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pytmanhattanHeuristicõs cCs;|}|j}|d|dd|d|dddS(s<The Euclidean distance heuristic for a PositionSearchProblemiiigà?(R6(R\R,R]R^R_((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyteuclideanHeuristicûs tCornersProblemcBs;eZdZd„Zd„Zd„Zd„Zd„ZRS(s’ This search problem finds paths through all four corners of a layout. You must select a suitable state space and successor function cCs½|jƒ|_|jƒ|_|jjd|jjd}}dd|f|df||ff|_x2|jD]'}|j|Œspdt|ƒGHqpqpWd|_ |j|jf|_ dS(sK Stores the walls, pacman's starting position and corners. iisWarning: no food in corner iN(ii( R2R3R4tstartingPositiontheighttwidthtcornersR:tstrR%R5(RtstartingGameStatettoptrighttcorner((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR$ s!' cCs|jS(sNReturns the start state (in your state space, not the full Pacman state space)(R5(R((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR@scCsdGHt|dƒdkS(s@Returns whether this search state is a goal state of the problems -------------ii(R0(RR((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRF#scsg}xëtjtjtjtjgD]Ë}|d\}}tj|ƒ\}}t||ƒt||ƒf\}} |j|| } | t krð|| f‰t ‡fd†|dDƒƒ} ˆ| f} d} | || f}|j |ƒnq%W|j d7_ |SdG|GHdGH(s« Returns successor states, the actions they require, and a cost of 1. As noted in search.py: For a given state, this should return a list of triples, (successor, action, stepCost), where 'successor' is a successor to the current state, 'action' is the action required to get there, and 'stepCost' is the incremental cost of expanding to that successor ic3s!|]}|ˆkr|VqdS(N((t.0Rk(t nextPosition(sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pys Osis successors:s/###############################################( RRGRHRIRRRJRKR3tFalsettupleRCR%(RRRMRNRRORPRQRRRSthitsWallt nextCornersRTtstept nextSuccessor((RmsfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRV)s"%&     cCs…|dkrdS|j\}}xY|D]Q}tj|ƒ\}}t||ƒt||ƒ}}|j||r&dSq&Wt|ƒS(s§ Returns the cost of a particular sequence of actions. If those actions include an illegal move, return 999999. This is implemented for you. i?BN(R&RcRRJRKR3R0(RR)RRORNRPRQ((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR*^s  !(R R R R$R@RFRVR*(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRbs     5cCs\|j}|j}d}|d}x-|dD]!}ttj||ƒ|ƒ}q-W|}|S(sª A heuristic for the CornersProblem that you defined. state: The current search state (a data structure you chose in your search problem) problem: The CornersProblem instance for this layout. This function should always return a number that is a lower bound on the shortest path from the state to a goal of the problem; i.e. it should be admissible (as well as consistent). ii(RfR3tmaxtutiltmanhattanDistance(RR,RfR3t maxDistanceR\Rktresult((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pytcornersHeuristicls   tAStarCornersAgentcBseZdZd„ZRS(sCA SearchAgent for FoodSearchProblem using A* and your foodHeuristiccCsd„|_t|_dS(NcSstj|tƒS(N(Rt aStarSearchRy(R#((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRŠs(RRbR!(R((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR$‰s (R R R R$(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRz‡stFoodSearchProblemcBs;eZdZd„Zd„Zd„Zd„Zd„ZRS(su A search problem associated with finding the a path that collects all of the food (dots) in a Pacman game. A search state in this problem is a tuple ( pacmanPosition, foodGrid ) where pacmanPosition: a tuple (x,y) of integers specifying Pacman's position foodGrid: a Grid (see game.py) of either True or False, specifying remaining food cCsI|jƒ|jƒf|_|jƒ|_||_d|_i|_dS(Ni(R4tgetFoodR>R2R3RhR%t heuristicInfo(RRh((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR$–s   cCs|jS(N(R>(R((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR@scCs|djƒdkS(Nii(tcount(RR((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRF sc CsÜg}|jd7_xÀtjtjtjtjgD] }|d\}}tj|ƒ\}}t||ƒt||ƒ}} |j || s4|dj ƒ} t | || <|j || f| f|dfƒq4q4W|S(sDReturns successor states, the actions they require, and a cost of 1.ii( R%RRGRHRIRRRJRKR3tcopyRnRC( RRRMt directionRRORPRQRRRStnextFood((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRV£s%!)cCs†|jƒd\}}d}xc|D][}tj|ƒ\}}t||ƒt||ƒ}}|j||rtdS|d7}q#W|S(svReturns the cost of a particular sequence of actions. If those actions include an illegal move, return 999999ii?Bi(R@RRJRKR3(RR)RRORURNRPRQ((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR*±s !(R R R R$R@RFRVR*(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR|s     tAStarFoodSearchAgentcBseZdZd„ZRS(sCA SearchAgent for FoodSearchProblem using A* and your foodHeuristiccCsd„|_t|_dS(NcSstj|tƒS(N(RR{t foodHeuristic(R#((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRÂs(RR|R!(R((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR$Ás (R R R R$(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRƒ¿scCs|\}}|jƒS(sç Your heuristic for the FoodSearchProblem goes here. This heuristic must be consistent to ensure correctness. First, try to come up with an admissible heuristic; almost all admissible heuristics will be consistent as well. If using A* ever finds a solution that is worse uniform cost search finds, your heuristic is *not* consistent, and probably not admissible! On the other hand, inadmissible or inconsistent heuristics may find optimal solutions, so be careful. The state is a tuple ( pacmanPosition, foodGrid ) where foodGrid is a Grid (see game.py) of either True or False. You can call foodGrid.asList() to get a list of food coordinates instead. If you want access to info like walls, capsules, etc., you can query the problem. For example, problem.walls gives you a Grid of where the walls are. If you want to *store* information to be reused in other calls to the heuristic, there is a dictionary called problem.heuristicInfo that you can use. For example, if you only want to count the walls once and store that value, try: problem.heuristicInfo['wallCount'] = problem.walls.count() Subsequent calls to this heuristic can access problem.heuristicInfo['wallCount'] (R(RR,R\tfoodGrid((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR„Ås tClosestDotSearchAgentcBs eZdZd„Zd„ZRS(s0Search for all food using a sequence of searchescCsÎg|_|}x|jƒjƒdkr®|j|ƒ}|j|7_x`|D]X}|jƒ}||kr•t|ƒt|ƒf}td|‚n|jd|ƒ}qOWqWd|_dt |jƒGHdS(Nis5findPathToClosestDot returned an illegal move: %s! %ssPath found with cost %d.( R)R}RtfindPathToClosestDottgetLegalActionsRgR'tgenerateSuccessorR/R0(RRt currentStatetnextPathSegmentRNtlegaltt((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR.äs     cCs>|jƒ}|jƒ}|jƒ}t|ƒ}tjƒdS(sNReturns a path (a list of actions) to the closest dot, starting from gameStateN(R4R}R2tAnyFoodSearchProblemRutraiseNotDefined(RR=t startPositiontfoodR3R,((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR‡ós     (R R R R.R‡(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR†âs RŽcBs eZdZd„Zd„ZRS(sù A search problem for finding a path to any food. This search problem is just like the PositionSearchProblem, but has a different goal test, which you need to fill in below. The state space and successor function do not need to be changed. The class definition above, AnyFoodSearchProblem(PositionSearchProblem), inherits the methods of the PositionSearchProblem. You can use this search problem to help you fill in the findPathToClosestDot method. cCsZ|jƒ|_|jƒ|_|jƒ|_d„|_igd|_|_|_ dS(sFStores information from the gameState. You don't need to change this.cSsdS(Ni((R((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRsiN( R}R‘R2R3R4R5R7R;R<R%(RR=((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR$ s  cCs|\}}tjƒdS(s‚ The state is Pacman's position. Fill this in with a goal test that will complete the problem definition. N(RuR(RRRRO((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRFs (R R R R$RF(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyRŽþs  tApproximateSearchAgentcBs eZdZd„Zd„ZRS(sGImplement your contest entry here. Change anything but the class name.cCsdS(s0This method is called before any moves are made.N((RR((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR.)scCstjƒdS(s From game.py: The Agent will receive a GameState and must return an action from Directions.{North, South, East, West, Stop} N(RuR(RR((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR -s(R R R R.R (((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyR’&s c Cs™|\}}|\}}|jƒ}||| s@td|‚||| sbtdt|ƒ‚t|d|d|dtdtƒ}ttj|ƒƒS(sR Returns the maze distance between any two points, using the search functions you have already built. The gameState can be any game state -- Pacman's position in that state is ignored. Example usage: mazeDistance( (2,4), (5,6), gameState) This might be a useful helper function for your ApproximateSearchAgent. spoint1 is a wall: spoint2 is a wall: R>R6R?R8(R2tAssertionErrorRgRRnR0Rtbfs( tpoint1tpoint2R=tx1ty1tx2ty2R3R#((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyt mazeDistance6s   "$((R tgameRRRRuR(RRR t SearchProblemRRWRZR`RaRbRyRzR|RƒR„R†RŽR’R›(((sfC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\01 Search Project\search\searchAgents.pyt s.   I]   g 2 (