from util import manhattanDistance from game import Directions import random import util from typing import Any, DefaultDict, List, Set, Tuple 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 __init__(self): self.lastPositions = [] self.dc = None def getAction(self, gameState: 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} ------------------------------------------------------------------------------ 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(agentIndex): 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 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] # Pick randomly among the best chosenIndex = random.choice(bestIndices) return legalMoves[chosenIndex] def evaluationFunction(self, currentGameState: GameState, action: str) -> float: """ The evaluation function takes in the current GameState (defined in pacman.py) and a proposed action and returns a rough estimate of the resulting successor GameState's value. 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: 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) ###################################################################################### # Problem 1b: implementing minimax class MinimaxAgent(MultiAgentSearchAgent): """ Your minimax agent (problem 1) """ def getAction(self, gameState: GameState) -> str: """ 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. Don't forget to limit the search depth using self.depth. Also, avoid modifying self.depth directly (e.g., when implementing depth-limited search) since it is a member variable that should stay fixed throughout runtime. 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.getScore(): Returns the score corresponding to the current state of the game 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_CODE (our solution is 22 lines of code, but don't worry if you deviate from this) raise Exception("Not implemented yet") # END_YOUR_CODE ###################################################################################### # Problem 2a: implementing alpha-beta class AlphaBetaAgent(MultiAgentSearchAgent): """ Your minimax agent with alpha-beta pruning (problem 2) You may reference the pseudocode for Alpha-Beta pruning here: en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#Pseudocode """ def getAction(self, gameState: GameState) -> str: """ Returns the minimax action using self.depth and self.evaluationFunction """ # BEGIN_YOUR_CODE (our solution is 43 lines of code, but don't worry if you deviate from this) raise Exception("Not implemented yet") # END_YOUR_CODE ###################################################################################### # Problem 3b: implementing expectimax class ExpectimaxAgent(MultiAgentSearchAgent): """ Your expectimax agent (problem 3) """ def getAction(self, gameState: GameState) -> str: """ 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_CODE (our solution is 20 lines of code, but don't worry if you deviate from this) raise Exception("Not implemented yet") # END_YOUR_CODE ###################################################################################### # Problem 4a (extra credit): creating a better evaluation function def betterEvaluationFunction(currentGameState: GameState) -> float: """ Your extreme, unstoppable evaluation function (problem 4). Note that you can't fix a seed in this function. """ # BEGIN_YOUR_CODE (our solution is 16 lines of code, but don't worry if you deviate from this) raise Exception("Not implemented yet") # END_YOUR_CODE # Abbreviation better = betterEvaluationFunction