submission.py

# ID: 20170499 NAME: Park Kyungphil
######################################################################################
# Problem 2a
# minimax value of the root node: 6
# pruned edges: h, m, x
######################################################################################

from collections import deque
from util import manhattanDistance
from game import Directions
import random
import util

from game import Agent


class ReflexAgent(Agent):
    """
      A reflex agent chooses an action at each choice point by examining
      its alternatives via a state evaluation function.

      The code below is provided as a guide.  You are welcome to change
      it in any way you see fit, so long as you don't touch our method
      headers.
    """

    def __init__(self):
        self.lastPositions = []
        self.dc = None

    def getAction(self, gameState):
        """
        getAction chooses among the best options according to the evaluation function.

        getAction takes a GameState and returns some Directions.X for some X in the set {North, South, West, East, Stop}
        ------------------------------------------------------------------------------
        Description of GameState and helper functions:

        A GameState specifies the full game state, including the food, capsules,
        agent configurations and score changes. In this function, the |gameState| argument
        is an object of GameState class. Following are a few of the helper methods that you
        can use to query a GameState object to gather information about the present state
        of Pac-Man, the ghosts and the maze.

        gameState.getLegalActions():
            Returns the legal actions for the agent specified. Returns Pac-Man's legal moves by default.

        gameState.generateSuccessor(agentIndex, action):
            Returns the successor state after the specified agent takes the action.
            Pac-Man is always agent 0.

        gameState.getPacmanState():
            Returns an AgentState object for pacman (in game.py)
            state.configuration.pos gives the current position
            state.direction gives the travel vector

        gameState.getGhostStates():
            Returns list of AgentState objects for the ghosts

        gameState.getNumAgents():
            Returns the total number of agents in the game

        gameState.getScore():
            Returns the score corresponding to the current state of the game
            It corresponds to Utility(s)


        The GameState class is defined in pacman.py and you might want to look into that for
        other helper methods, though you don't need to.
        """
        # Collect legal moves and successor states
        legalMoves = gameState.getLegalActions()

        # Choose one of the best actions
        scores = [self.evaluationFunction(gameState, action) for action in legalMoves]
        bestScore = max(scores)
        bestIndices = [index for index in range(len(scores)) if scores[index] == bestScore]
        chosenIndex = random.choice(bestIndices)  # Pick randomly among the best

        return legalMoves[chosenIndex]

    def evaluationFunction(self, currentGameState, action):
        """
        The evaluation function takes in the current and proposed successor
        GameStates (pacman.py) and returns a number, where higher numbers are better.

        The code below extracts some useful information from the state, like the
        remaining food (oldFood) and Pacman position after moving (newPos).
        newScaredTimes holds the number of moves that each ghost will remain
        scared because of Pacman having eaten a power pellet.
        """
        # Useful information you can extract from a GameState (pacman.py)
        successorGameState = currentGameState.generatePacmanSuccessor(action)
        newPos = successorGameState.getPacmanPosition()
        oldFood = currentGameState.getFood()
        newGhostStates = successorGameState.getGhostStates()
        newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]

        return successorGameState.getScore()


def scoreEvaluationFunction(currentGameState):
    """
      This default evaluation function just returns the score of the state.
      The score is the same one displayed in the Pacman GUI.

      This evaluation function is meant for use with adversarial search agents
      (not reflex agents).
    """
    return currentGameState.getScore()


class MultiAgentSearchAgent(Agent):
    """
      This class provides some common elements to all of your
      multi-agent searchers.  Any methods defined here will be available
      to the MinimaxPacmanAgent, AlphaBetaPacmanAgent & ExpectimaxPacmanAgent.

      You *do not* need to make any changes here, but you can if you want to
      add functionality to all your adversarial search agents.  Please do not
      remove anything, however.

      Note: this is an abstract class: one that should not be instantiated.  It's
      only partially specified, and designed to be extended.  Agent (game.py)
      is another abstract class.
    """

    def __init__(self, evalFn='scoreEvaluationFunction', depth='2'):
        self.index = 0  # Pacman is always agent index 0
        self.evaluationFunction = util.lookup(evalFn, globals())
        self.depth = int(depth)

######################################################################################
# Problem 1a: implementing minimax


class MinimaxAgent(MultiAgentSearchAgent):
    """
      Your minimax agent (problem 1)
    """

    def getAction(self, gameState):
        """
          Returns the minimax action from the current gameState using self.depth
          and self.evaluationFunction. Terminal states can be found by one of the following:
          pacman won, pacman lost or there are no legal moves.

          Here are some method calls that might be useful when implementing minimax.

          gameState.getLegalActions(agentIndex):
            Returns a list of legal actions for an agent
            agentIndex=0 means Pacman, ghosts are >= 1

          Directions.STOP:
            The stop direction, which is always legal

          gameState.generateSuccessor(agentIndex, action):
            Returns the successor game state after an agent takes an action

          gameState.getNumAgents():
            Returns the total number of agents in the game

          gameState.getScore():
            Returns the score corresponding to the current state of the game
            It corresponds to Utility(s)

          gameState.isWin():
            Returns True if it's a winning state

          gameState.isLose():
            Returns True if it's a losing state

          self.depth:
            The depth to which search should continue
        """

        # BEGIN_YOUR_ANSWER (our solution is 30 lines of code, but don't worry if you deviate from this)
        def max_value(gameState, depth):
            value = [float('-inf'), Directions.STOP]
            for act in gameState.getLegalActions(0):
                res = miniMax(gameState.generateSuccessor(0, act), depth+1, 1)
                if value[0] < res:
                    value = [res, act]
            return value

        def min_value(gameState, depth, agentIndex):
            value = float('inf')
            if agentIndex == gameState.getNumAgents()-1:
                for act in gameState.getLegalActions(agentIndex):
                    value = min(value, miniMax(gameState.generateSuccessor(agentIndex, act), depth+1, 0))
                return value
            else:
                for act in gameState.getLegalActions(agentIndex):
                    value = min(value, miniMax(gameState.generateSuccessor(agentIndex, act), depth, agentIndex+1))
                return value

        def miniMax(gameState, depth, agentIndex):
            if depth == self.depth*2:
                return gameState.getScore()
            if gameState.isLose() or gameState.isWin():
                return gameState.getScore()
            if agentIndex == 0:
                return max_value(gameState, depth)[0]
            else:
                return min_value(gameState, depth, agentIndex)
        return max_value(gameState, 0)[1]
        # END_YOUR_ANSWER
######################################################################################
# Problem 2b: implementing alpha-beta


class AlphaBetaAgent(MultiAgentSearchAgent):
    """
      Your minimax agent with alpha-beta pruning (problem 2)
    """

    def getAction(self, gameState):
        """
          Returns the minimax action using self.depth and self.evaluationFunction
        """

        # BEGIN_YOUR_ANSWER (our solution is 42 lines of code, but don't worry if you deviate from this)
        def max_value(gameState, depth, partial_min, partial_max):
            value = [float('-inf'), Directions.STOP]
            for act in gameState.getLegalActions(0):
                if value[0] > partial_min:
                    return value
                res = miniMax(gameState.generateSuccessor(0, act), depth+1, 1, partial_min, partial_max)
                if value[0] < res:
                    value = [res, act]
                partial_max = max(partial_max, value[0])
            return value

        def min_value(gameState, depth, agentIndex, partial_min, partial_max):
            value = float('inf')
            if agentIndex == gameState.getNumAgents()-1:
                for act in gameState.getLegalActions(agentIndex):
                    if value < partial_max:
                        return value
                    value = min(value, miniMax(gameState.generateSuccessor(agentIndex, act), depth+1, 0, partial_min, partial_max))
                    partial_min = min(partial_min, value)
                return value
            else:
                for act in gameState.getLegalActions(agentIndex):
                    if value != float('inf') and value < partial_max:
                        return value
                    value = min(value, miniMax(gameState.generateSuccessor(agentIndex, act), depth, agentIndex+1, partial_min, partial_max))
                    partial_min = min(partial_min, value)
                return value

        def miniMax(gameState, depth, agentIndex, partial_min, partial_max):
            if depth == self.depth*2:
                return gameState.getScore()
            if gameState.isLose() or gameState.isWin():
                return gameState.getScore()
            if agentIndex == 0:
                return max_value(gameState, depth, partial_min, partial_max)[0]
            else:
                return min_value(gameState, depth, agentIndex, partial_min, partial_max)
        # initiate get Action
        partial_min = float('inf')
        partial_max = float('-inf')
        return max_value(gameState, 0, partial_min, partial_max)[1]
        # END_YOUR_ANSWER

######################################################################################
# Problem 3a: implementing expectimax


class ExpectimaxAgent(MultiAgentSearchAgent):
    """
      Your expectimax agent (problem 3)
    """

    def getAction(self, gameState):
        """
          Returns the expectimax action using self.depth and self.evaluationFunction

          All ghosts should be modeled as choosing uniformly at random from their
          legal moves.
        """

        # BEGIN_YOUR_ANSWER (our solution is 30 lines of code, but don't worry if you deviate from this)
        def max_value(gameState, depth):
            value = [float('-inf'), Directions.STOP]
            for act in gameState.getLegalActions(0):
                res = miniMax(gameState.generateSuccessor(0, act), depth+1, 1)
                if value[0] < res:
                    value = [res, act]
            return value

        def exp_value(gameState, depth, agentIndex):
            actionNum = 0
            totValue = 0
            if agentIndex == gameState.getNumAgents()-1:
                for act in gameState.getLegalActions(agentIndex):
                    actionNum += 1
                    totValue += miniMax(gameState.generateSuccessor(agentIndex, act), depth+1, 0)
                return totValue / actionNum
            else:
                for act in gameState.getLegalActions(agentIndex):
                    actionNum += 1
                    totValue += miniMax(gameState.generateSuccessor(agentIndex, act), depth, agentIndex+1)
                return totValue / actionNum

        def miniMax(gameState, depth, agentIndex):
            if depth == self.depth*2:
                return self.evaluationFunction(gameState)
            if gameState.isLose() or gameState.isWin():
                return self.evaluationFunction(gameState)
            if agentIndex == 0:
                return max_value(gameState, depth)[0]
            else:
                return exp_value(gameState, depth, agentIndex)
        return max_value(gameState, 0)[1]
        # END_YOUR_ANSWER

######################################################################################
# Problem 4a (extra credit): creating a better evaluation function


def betterEvaluationFunction(currentGameState):
    """
    Your extreme, unstoppable evaluation function (problem 4).
    """

    # BEGIN_YOUR_ANSWER (our solution is 60 lines of code, but don't worry if you deviate from this)
    def bfs(currentGameState):
        check = [[False]*100 for i in range(100)]
        q = deque()
        q.append([currentGameState, 0])
        cur_x, cur_y = currentGameState.getPacmanPosition()
        check[cur_x][cur_y] = True
        while q:
            cur_gameState, cur_Num = q.popleft()
            for act in cur_gameState.getLegalActions(0):
                newGameState = cur_gameState.generateSuccessor(0, act)
                nx, ny = newGameState.getPacmanPosition()
                if not check[nx][ny]:
                    check[nx][ny] = True
                    for capsule in currentGameState.getCapsules():
                        cap_x, cap_y = capsule
                        if nx == cap_x and ny == cap_y:
                            return cur_Num+1
                    if currentGameState.hasFood(nx, ny):
                        return cur_Num+7
                    q.append([newGameState, cur_Num+1])
        return 40
    score = scoreEvaluationFunction(currentGameState)
    return score-bfs(currentGameState)
# END_YOUR_ANSWER


# Abbreviation
better = betterEvaluationFunction