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agents.py
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agents.py
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import random
import copy
from collections import Counter
from featureExtractor import FeatureExtractor
from bcolors import bcolors
class Player:
def __init__(self, name):
self.name = name
self.cards = [] #(cardValue, cardElement)
self.accumulatedCards = {"Fire": 0, "Water": 0, "Ice": 0}
self.playedCard = None
def pickCard(self, index):
# Takes in the index of the card to be picked in self.cards
self.playedCard = self.cards[index]
self.cards.pop(index)
return
def resetForNewGame(self):
self.cards = []
self.accumulatedCards = {"Fire": 0, "Water": 0, "Ice": 0}
self.playedCard = None
# from main import judgeStreak, judgeThreeOfAKind
class GreedyAgent(Player):
def pickCard(self):
currentCards = {"Fire": [], "Water": [], "Ice": []}
for c in self.cards:
currentCards[c[1]].append(c)
pickedElement = self.checkForThreeOfAKind(currentCards)
if pickedElement == "":
if self.accumulatedCards["Fire"] + len(currentCards["Fire"]) >= 3:
pickedElement = "Fire"
elif self.accumulatedCards["Water"] + len(currentCards["Water"]) >= 3:
pickedElement = "Water"
elif self.accumulatedCards["Ice"] + len(currentCards["Ice"]) >= 3:
pickedElement = "Ice"
if pickedElement:
card = max(currentCards[pickedElement], key=lambda x: x[0])
else:
card = max(self.cards, key=lambda x: x[0])
self.playedCard = card
self.cards.remove(card)
return
def checkForThreeOfAKind(self, currentCards):
pickedElement = ""
elements = ["Fire", "Ice", "Water"]
i = 0
while len(elements) > 1 and i < len(elements):
if self.accumulatedCards[elements[i]] > 0:
elements.pop(i)
else:
i += 1
# print("elements", elements)
if len(elements) == 1:
# print("currentCards", currentCards)
if len(currentCards[elements[0]]) > 0:
pickedElement = elements[0]
# print("elements", elements)
# print("pickedElement after check()", pickedElement)
return pickedElement
class RandomAgent(Player):
def pickCard(self):
card = random.choice(self.cards)
self.playedCard = card
self.cards.remove(card)
class ApproximateQLearningAgent(Player):
def __init__(self, name, epsilon=0.05, gamma=0.8, alpha=0.2, numTraining=0):
self.name = name
self.cards = [] #(cardValue, cardElement)
self.accumulatedCards = {"Fire": 0, "Water": 0, "Ice": 0}
self.playedCard = None
self.args = {}
self.args['epsilon'] = epsilon
self.args['gamma'] = gamma
self.args['alpha'] = alpha
self.args['numTraining'] = numTraining
self.weights = Counter()
# self.weights["enemy-distance-to-closest-win"] = 1.3999995454998298e-06
# self.weights["agent-distance-to-closest-win"] = 1.299999463999758e-06
self.weights["enemy-distance-to-closest-win"] = -4.120535635213156
self.weights["agent-distance-to-closest-win"] = 9.586679017815417
self.weights["agent-went-closer-to-win"] = -0.9656494587969497
self.weights["agent-can-block-enemy-advancement"] = 15.147299275663869
self.featExtractor = FeatureExtractor()
self.lastState = None
self.lastAction = None
self.lastScore = 0
def resetForNewGame(self):
self.cards = []
self.accumulatedCards = {"Fire": 0, "Water": 0, "Ice": 0}
self.playedCard = None
self.lastState = None
self.lastAction = None
self.lastScore = 0
def pickCard(self, card):
# Takes in the index of the card to be picked in self.cards
if card not in self.cards:
print(card)
print(self.cards)
raise ValueError('Picked card not in current cards!')
self.playedCard = card
self.cards.remove(card)
return
def getLegalActions(self, gameState):
if self.name == gameState.p1.name:
return gameState.p1.cards
else:
return gameState.p2.cards
return self.cards
def getQValue(self, gameState, action):
"""
Should return Q(gameState,action) = w * featureVector
where * is the dotProduct operator
"""
result = 0
features = self.featExtractor.getFeatures(gameState, action, self.name)
for feature in features:
result += features[feature] * self.weights[feature]
return result
def flipCoin(self, prob):
r = random.random()
return r < prob
def computeActionFromQValues(self, gameState):
actions = self.getLegalActions(gameState)
max_action = None
max_q_val = float("inf")
if not actions:
return None
for a in actions:
q_val = self.getQValue(gameState, a)
if q_val < max_q_val:
max_q_val = q_val
max_action = a
if max_action is None:
return random.choice(actions)
else:
return max_action
def doAction(self, gameState):
legalActions = self.getLegalActions(gameState)
action = None
"*** YOUR CODE HERE ***"
# if not legalActions:
# action = None
# else:
coinflip = self.flipCoin(self.args["epsilon"])
if coinflip:
action = random.choice(legalActions)
elif not coinflip:
action = self.computeActionFromQValues(gameState)
# print("setting lastState")
self.lastState = copy.deepcopy(gameState)
# print("self.lastState",self.lastState)
self.lastAction = copy.deepcopy(action)
return action
def update(self, gameState, score):
"""
Should update your weights based on transition
"""
# deleted action, nextState, reward from params
# print("self.lastState",self.lastState)
if self.lastState is not None:
state, action, nextState, deltaReward = self.lastState, self.lastAction, gameState, score - self.lastScore
actions = self.getLegalActions(nextState)
max_qval_action = (float("-inf"), None)
if not actions:
max_qval_action = 0
elif actions:
for a in actions:
#! going through all the actions to get max action
q_val = self.getQValue(nextState, a)
max_qval_action = max(max_qval_action, (q_val, a), key=lambda x: x[0])
difference = deltaReward + self.args['gamma'] * max_qval_action[0] - self.getQValue(state, action)
features = self.featExtractor.getFeatures(state, action, self.name)
for feature in features:
# print(feature, "weights:", self.weights[feature])
self.weights[feature] = self.weights[feature] + self.args['alpha'] * difference * features[feature]
self.lastScore = score
def printEpisodeInfo(self):
print(bcolors.OKBLUE + "AQL score:", str(self.lastScore))
print(bcolors.OKBLUE + "AQL accumulated cards:", str(self.accumulatedCards))
# print(bcolors.OKBLUE + "AQL weights:")
# for key, value in self.weights.items():
# print(" ",key, value)
print( bcolors.ENDC)