# 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 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 getAction(self, 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] # Pick randomly among the best chosenIndex = random.choice(bestIndices) "Add more of your code here if you want to" return legalMoves[chosenIndex] def evaluationFunction(self, currentGameState, 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] # print(newPos) # print(newFood) # print(newGhostStates) # print(newScaredTimes) # make is so that if it is closer to a ghost then reduce the score # increase if closer to food # only one ghost in classic map ghost_pos = newGhostStates[0].getPosition() nearest_ghost_dist = manhattanDistance(ghost_pos, newPos) # nearest food all_food_pos = newFood.asList() score = successorGameState.getScore() if len(all_food_pos) and nearest_ghost_dist: nearest_food_dist = min([manhattanDistance( foodPos, newPos) for foodPos in all_food_pos]) score = score - (5 / nearest_ghost_dist) + (10 / nearest_food_dist) "*** YOUR CODE HERE ***" return score 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) class MinimaxAgent(MultiAgentSearchAgent): """ Your minimax agent (question 2) """ def getAction(self, 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 ***" # Use function for min and max of node # need same args to call main_delegation again after agents turn # mechanism to keep track with agenCount whether ghost or pacman INF, NEG_INF = float("inf"), -float("inf") def min_recurse(depth, agentCount, gameState): # action value pair best_action = "" best_value = INF node_actions = gameState.getLegalActions(agentCount) # base if not node_actions: # print(self.depth) return self.evaluationFunction(gameState) for successor in node_actions: curr_succ = gameState.generateSuccessor(agentCount, successor) value_node = main_delegation(depth, agentCount + 1, curr_succ) if type(value_node) is list: updated_action = value_node[1] else: updated_action = value_node # update best_action if updated_action < best_value: best_action = successor best_value = updated_action return [best_action, best_value] def max_recurse(depth, agentCount, gameState): # action value pair best_action = "" best_value = NEG_INF node_actions = gameState.getLegalActions(agentCount) # base if not node_actions: # print(self.depth) return self.evaluationFunction(gameState) for successor in node_actions: curr_succ = gameState.generateSuccessor(agentCount, successor) value_node = main_delegation(depth, agentCount + 1, curr_succ) if type(value_node) is list: updated_action = value_node[1] else: updated_action = value_node # update best_action if updated_action > best_value: best_action = successor best_value = updated_action return [best_action, best_value] # main recurring function depending on who the player is def main_delegation(depth, agentCount, gameState): # see if all ghosts or agent recursed this time iterAgentCount = gameState.getNumAgents() if iterAgentCount <= agentCount: agentCount = 0 depth += 1 # stopping mechanisms if depth == self.depth: return self.evaluationFunction(gameState) if gameState.isWin() or gameState.isLose(): return self.evaluationFunction(gameState) if agentCount == 0: return max_recurse(depth, agentCount, gameState) else: return min_recurse(depth, agentCount, gameState) return main_delegation(0, 0, gameState)[0] class AlphaBetaAgent(MultiAgentSearchAgent): """ Your minimax agent with alpha-beta pruning (question 3) """ def getAction(self, gameState): """ Returns the minimax action using self.depth and self.evaluationFunction """ "*** YOUR CODE HERE ***" INF, NEG_INF = float("inf"), -float("inf") def min_recurse(depth, agentCount, gameState, alpha, beta): # action value pair best_action = "" best_value = INF node_actions = gameState.getLegalActions(agentCount) # base if not node_actions: # print(self.depth) return self.evaluationFunction(gameState) for successor in node_actions: curr_succ = gameState.generateSuccessor(agentCount, successor) value_node = main_delegation(depth, agentCount + 1, curr_succ, alpha, beta) if type(value_node) is list: updated_action = value_node[1] else: updated_action = value_node # update best_action if updated_action < best_value: best_action = successor best_value = updated_action # pruning if updated_action < alpha: return [successor, updated_action] beta = min([beta, updated_action]) return [best_action, best_value] def max_recurse(depth, agentCount, gameState, alpha, beta): # action value pair best_action = "" best_value = NEG_INF node_actions = gameState.getLegalActions(agentCount) # base if not node_actions: # print(self.depth) return self.evaluationFunction(gameState) for successor in node_actions: curr_succ = gameState.generateSuccessor(agentCount, successor) value_node = main_delegation(depth, agentCount + 1, curr_succ, alpha, beta) if type(value_node) is list: updated_action = value_node[1] else: updated_action = value_node # update best_action if updated_action > best_value: best_action = successor best_value = updated_action # pruning if updated_action > beta: return [successor, updated_action] alpha = max([alpha, updated_action]) return [best_action, best_value] # main recurring function depending on who the player is def main_delegation(depth, agentCount, gameState, alpha, beta): # see if all ghosts or agent recursed this time iterAgentCount = gameState.getNumAgents() if iterAgentCount <= agentCount: agentCount = 0 depth += 1 # stopping mechanisms if depth == self.depth: return self.evaluationFunction(gameState) if gameState.isWin() or gameState.isLose(): return self.evaluationFunction(gameState) if agentCount == 0: return max_recurse(depth, agentCount, gameState, alpha, beta) else: return min_recurse(depth, agentCount, gameState, alpha, beta) return main_delegation(0, 0, gameState, NEG_INF, INF)[0] class ExpectimaxAgent(MultiAgentSearchAgent): """ Your expectimax agent (question 4) """ def max_recurse(self, gameState, depth): node_actions = gameState.getLegalActions(0) if not node_actions: return self.evaluationFunction(gameState), None if depth > self.depth or gameState.isWin(): return self.evaluationFunction(gameState), None costs = [] for successor in node_actions: curr_succ = gameState.generateSuccessor(0, successor) costs.append((self.expected_recurse(curr_succ, 1, depth)[0], successor)) return max(costs) def expected_recurse(self, gameState, agentCount, depth): node_actions = gameState.getLegalActions(agentCount) if not node_actions: return self.evaluationFunction(gameState), None if gameState.isLose(): return self.evaluationFunction(gameState), None costs = [] for successor in node_actions: curr_succ = gameState.generateSuccessor(agentCount, successor) iterAgents = gameState.getNumAgents() - 1 if iterAgents == agentCount: costs.append(self.max_recurse(curr_succ, depth + 1)) else: costs.append(self.expected_recurse(curr_succ, agentCount + 1, depth)) # calculating averages for optimization sum_costs = map(lambda x: float(x[0]) / len(costs), costs) return sum(sum_costs), None 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. """ "*** YOUR CODE HERE ***" result = self.max_recurse(gameState, 1) return result[1] def betterEvaluationFunction(currentGameState): """ Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable evaluation function (question 5). DESCRIPTION: """ "*** YOUR CODE HERE ***" ghostStates = currentGameState.getGhostStates() totalScore = currentGameState.getScore() pacPos = currentGameState.getPacmanPosition() all_food = currentGameState.getFood().asList() # find the furthest food distance to make it reach that pellet faster all_dist = [1.0 / manhattanDistance(foodPos, pacPos) for foodPos in all_food] # for last state all_dist.append(0) return max(all_dist) + totalScore # Abbreviation better = betterEvaluationFunction