# multiAgents.py
# --------------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).


from util import manhattanDistance
from game import Directions
import random, util

from game import Agent
from pacman import GameState

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 getAction(self, gameState: GameState):
        """
        You do not need to change this method, but you're welcome to.

        getAction chooses among the best options according to the evaluation function.

        Just like in the previous project, getAction takes a GameState and returns
        some Directions.X for some X in the set {NORTH, SOUTH, WEST, EAST, STOP}
        """
        # 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

        "Add more of your code here if you want to"

        return legalMoves[chosenIndex]

    def evaluationFunction(self, currentGameState: GameState, action):
        """
        Design a better evaluation function here.

        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 (newFood) 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.

        Print out these variables to see what you're getting, then combine them
        to create a masterful evaluation function.
        """
        # Useful information you can extract from a GameState (pacman.py)
        successorGameState = currentGameState.generatePacmanSuccessor(action)
        newPos = successorGameState.getPacmanPosition()
        newFood = successorGameState.getFood()
        newGhostStates = successorGameState.getGhostStates()
        newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]

        "*** YOUR CODE HERE ***"
        # 计算到最近食物的距离
        # Calculate distance to the nearest food
        foodList = newFood.asList()
        if not foodList:
            return 999999

        minFoodDist = min([util.manhattanDistance(newPos, food) for food in foodList])

        # 计算到幽灵的距离
        # Calculate distance to ghosts
        for i, ghostState in enumerate(newGhostStates):
            ghostPos = ghostState.getPosition()
            dist = util.manhattanDistance(newPos, ghostPos)
            # 如果幽灵太近且不处于惊吓状态，逃跑！
            # If ghost is too close and not scared, run away!
            if dist < 2 and newScaredTimes[i] == 0:
                return -999999

        # 结合分数和食物距离
        # 我们优先考虑生存（上面已处理），然后是分数，然后是靠近食物
        # Combine score and food distance
        # We prioritize survival (handled above) then score, then getting closer to food
        return successorGameState.getScore() + 1.0 / (minFoodDist + 1)

def scoreEvaluationFunction(currentGameState: GameState):
    """
    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)

class MinimaxAgent(MultiAgentSearchAgent):
    """
    Your minimax agent (question 2)
    """

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

        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

        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.isWin():
        Returns whether or not the game state is a winning state

        gameState.isLose():
        Returns whether or not the game state is a losing state
        """
        "*** YOUR CODE HERE ***"
        # 核心 minimax 算法函数
        def minimax(agentIndex, depth, gameState):
            # 终止条件：达到指定深度，或者游戏胜利/失败
            if gameState.isWin() or gameState.isLose() or depth == self.depth:
                return self.evaluationFunction(gameState)

            # 也就是 Pacman (MAX agent)
            if agentIndex == 0:
                return maxLevel(agentIndex, depth, gameState)
            # 也就是 Ghosts (MIN agents)
            else:
                return minLevel(agentIndex, depth, gameState)

        # Max 层 (Pacman)
        def maxLevel(agentIndex, depth, gameState):
            bestValue = float("-inf")
            # 遍历 Pacman 的所有合法动作
            for action in gameState.getLegalActions(agentIndex):
                successorGameState = gameState.generateSuccessor(agentIndex, action)
                # 下一个是第一个幽灵 (agentIndex + 1)
                value = minimax(agentIndex + 1, depth, successorGameState)
                bestValue = max(bestValue, value)
            return bestValue

        # Min 层 (Ghosts)
        def minLevel(agentIndex, depth, gameState):
            bestValue = float("inf")
            # 遍历幽灵的所有合法动作
            for action in gameState.getLegalActions(agentIndex):
                successorGameState = gameState.generateSuccessor(agentIndex, action)
                # 如果是最后一个幽灵，下一个是 Pacman，且深度加 1
                if agentIndex == gameState.getNumAgents() - 1:
                    value = minimax(0, depth + 1, successorGameState)
                # 否则是下一个幽灵
                else:
                    value = minimax(agentIndex + 1, depth, successorGameState)
                bestValue = min(bestValue, value)
            return bestValue

        # getAction 主逻辑
        # 针对根节点 (Pacman at current depth) 选择最佳动作
        bestAction = None
        bestValue = float("-inf")
        
        for action in gameState.getLegalActions(0):
            successorGameState = gameState.generateSuccessor(0, action)
            # 从第一个幽灵开始计算
            value = minimax(1, 0, successorGameState)
            if value > bestValue:
                bestValue = value
                bestAction = action
        
        return bestAction

class AlphaBetaAgent(MultiAgentSearchAgent):
    """
    Your minimax agent with alpha-beta pruning (question 3)
    """

    def getAction(self, gameState: GameState):
        """
        Returns the minimax action using self.depth and self.evaluationFunction
        """
        "*** YOUR CODE HERE ***"
        # 核心 alpha-beta 算法函数
        def alphaBeta(agentIndex, depth, gameState, alpha, beta):
            # 终止条件：达到指定深度，或者游戏胜利/失败
            if gameState.isWin() or gameState.isLose() or depth == self.depth:
                return self.evaluationFunction(gameState)

            if agentIndex == 0:
                return maxValue(agentIndex, depth, gameState, alpha, beta)
            else:
                return minValue(agentIndex, depth, gameState, alpha, beta)

        # Max 值计算 (Pacman)
        def maxValue(agentIndex, depth, gameState, alpha, beta):
            v = float("-inf")
            for action in gameState.getLegalActions(agentIndex):
                successorGameState = gameState.generateSuccessor(agentIndex, action)
                v = max(v, alphaBeta(agentIndex + 1, depth, successorGameState, alpha, beta))
                # Pruning / 剪枝
                if v > beta:
                    return v
                alpha = max(alpha, v)
            return v

        # Min 值计算 (Ghosts)
        def minValue(agentIndex, depth, gameState, alpha, beta):
            v = float("inf")
            for action in gameState.getLegalActions(agentIndex):
                successorGameState = gameState.generateSuccessor(agentIndex, action)
                if agentIndex == gameState.getNumAgents() - 1:
                    v = min(v, alphaBeta(0, depth + 1, successorGameState, alpha, beta))
                else:
                    v = min(v, alphaBeta(agentIndex + 1, depth, successorGameState, alpha, beta))
                # Pruning / 剪枝
                if v < alpha:
                    return v
                beta = min(beta, v)
            return v

        # getAction 主逻辑
        bestAction = None
        v = float("-inf")
        alpha = float("-inf")
        beta = float("inf")

        for action in gameState.getLegalActions(0):
            successorGameState = gameState.generateSuccessor(0, action)
            score = alphaBeta(1, 0, successorGameState, alpha, beta)
            
            if score > v:
                v = score
                bestAction = action
            
            # 根节点的 alpha 更新
            if v > beta:
                return bestAction # 理论上根节点不会在这里剪枝，但保持逻辑一致
            alpha = max(alpha, v)
            
        return bestAction

class ExpectimaxAgent(MultiAgentSearchAgent):
    """
      Your expectimax agent (question 4)
    """

    def getAction(self, gameState: 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.
        """
        "*** YOUR CODE HERE ***"
        # 核心 expectimax 算法函数
        def expectimax(agentIndex, depth, gameState):
            if gameState.isWin() or gameState.isLose() or depth == self.depth:
                return self.evaluationFunction(gameState)
            
            if agentIndex == 0:
                return maxValue(agentIndex, depth, gameState)
            else:
                return expValue(agentIndex, depth, gameState)

        def maxValue(agentIndex, depth, gameState):
            v = float("-inf")
            for action in gameState.getLegalActions(agentIndex):
                successorGameState = gameState.generateSuccessor(agentIndex, action)
                v = max(v, expectimax(agentIndex + 1, depth, successorGameState))
            return v
        
        def expValue(agentIndex, depth, gameState):
            v = 0
            actions = gameState.getLegalActions(agentIndex)
            if not actions:
                return 0
            prob = 1.0 / len(actions)
            for action in actions:
                successorGameState = gameState.generateSuccessor(agentIndex, action)
                if agentIndex == gameState.getNumAgents() - 1:
                    v += prob * expectimax(0, depth + 1, successorGameState)
                else:
                    v += prob * expectimax(agentIndex + 1, depth, successorGameState)
            return v

        bestAction = None
        v = float("-inf")
        for action in gameState.getLegalActions(0):
            successorGameState = gameState.generateSuccessor(0, action)
            score = expectimax(1, 0, successorGameState)
            if score > v:
                v = score
                bestAction = action
        return bestAction

def betterEvaluationFunction(currentGameState: GameState):
    """
    Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable
    evaluation function (question 5).

    DESCRIPTION: <write something here so we know what you did>
    """
    "*** YOUR CODE HERE ***"
    # 获取有用的状态信息
    # Get useful information from the state
    newPos = currentGameState.getPacmanPosition()
    newFood = currentGameState.getFood()
    newGhostStates = currentGameState.getGhostStates()
    newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]
    newCapsules = currentGameState.getCapsules()

    # 基础分数
    score = currentGameState.getScore()

    # 食物距离评分
    foodList = newFood.asList()
    if foodList:
        minFoodDist = min([util.manhattanDistance(newPos, food) for food in foodList])
        score += 10.0 / (minFoodDist + 1) # 距离越近分数越高

    # 胶囊距离评分
    if newCapsules:
        minCapDist = min([util.manhattanDistance(newPos, cap) for cap in newCapsules])
        score += 20.0 / (minCapDist + 1)

    # 幽灵距离评分
    for i, ghostState in enumerate(newGhostStates):
        ghostPos = ghostState.getPosition()
        dist = util.manhattanDistance(newPos, ghostPos)
        if newScaredTimes[i] > 0:
            # 如果幽灵被吓坏了，我们要去吃它（靠近它）
            score += 100.0 / (dist + 1)
        else:
            # 否则，如果太近了，要扣分（远离它）
            if dist < 2:
                score -= 1000.0
            else:
                score -= 10.0 / (dist + 1) # 稍微远离一点

    # 剩余食物越少越好
    score -= len(foodList) * 4

    # 剩余胶囊越少越好
    score -= len(newCapsules) * 20

    return score

# Abbreviation
better = betterEvaluationFunction