def __init__(self, pacman_agent=agent.HumanAgent()):
self.pacman_agent = pacman_agent
pygame.init()
pygame.font.init()
self.screen = pygame.display.set_mode(config.SCREEN_SIZE)
self.game = game.Game(self.pacman_agent)
def getAgent(agentType, playerNum):
if agentType == 'human':
return agt.HumanAgent(playerNum)
elif agentType == 'random':
return agt.RandomAgent()
elif agentType == 'simple':
return agt.SimpleAgent()
elif agentType == 'reflex':
return agt.ReflexAgent(playerNum)
elif agentType == 'simple++':
return agt.SimpleEnhancedAgent(playerNum)
def create_player_list(args):
# Only need board_params and players in args
board_params = args["board_params"]
list_players = []
for i, player_args in enumerate(args["players"]):
kwargs = removekey(player_args, "agent")
if player_args["agent"] == "RandomAgent":
list_players.append(agent.RandomAgent(f"Random_{i}"))
elif player_args["agent"] == "PeacefulAgent":
list_players.append(agent.PeacefulAgent(f"Peaceful_{i}"))
elif player_args["agent"] == "FlatMCPlayer":
list_players.append(
agent.FlatMCPlayer(name=f'flatMC_{i}', **kwargs))
elif player_args["agent"] == "UCTPlayer":
list_players.append(agent.UCTPlayer(name=f'UCT_{i}', **kwargs))
elif player_args["agent"] == "PUCTPlayer":
world = World(board_params["path_board"])
board = Board(
world,
[agent.RandomAgent('Random1'),
agent.RandomAgent('Random2')])
board.setPreferences(board_params)
puct = load_puct(board, player_args)
list_players.append(puct)
elif player_args["agent"] == "NetPlayer":
world = World(board_params["path_board"])
board = Board(
world,
[agent.RandomAgent('Random1'),
agent.RandomAgent('Random2')])
board.setPreferences(board_params)
netPlayer = load_NetPlayer(board, player_args)
list_players.append(netPlayer)
elif player_args["agent"] == "Human":
hp_name = player_args["name"] if "name" in player_args else "human"
hp = agent.HumanAgent(name=hp_name)
list_players.append(hp)
return list_players
def main(args=None):
from optparse import OptionParser
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
parser.add_option("-p",
"--player1",
dest="player1",
default="random",
help="Choose type of first player")
(opts, args) = parser.parse_args(args)
evalArgs = load_weights()
evalFn = aiAgents.nnetEval
p1 = None
if opts.player1 == 'random':
p1 = agent.RandomAgent(game.Game.TOKENS[0])
elif opts.player1 == 'reflex':
p1 = aiAgents.TDAgent(game.Game.TOKENS[0], evalArgs)
elif opts.player1 == 'expectiminimax':
p1 = aiAgents.ExpectiMiniMaxAgent(game.Game.TOKENS[0], evalFn,
evalArgs)
elif opts.player1 == 'human':
p1 = agent.HumanAgent(game.Game.TOKENS[0])
# p2 = agent.RandomAgent(game.Game.TOKENS[1])
p2 = aiAgents.ExpectiMiniMaxAgent(game.Game.TOKENS[1], evalFn, evalArgs)
if p1 is None:
print "Please specify legitimate player"
import sys
sys.exit(1)
play([p1, p2])
import agent
import GUI
import MCTS
print('input side: \'black\' or \'white\'')
side = input()
print('input time limit for tree move (one number in seconds)')
move_time = float(input())
agent1 = MCTS.MCTS('../weights/gomoku_nn',
'../weights/gomoku_nn',
sim_number=10000,
linear=False,
time=move_time,
rollout_depth=10)
agent2 = agent.HumanAgent()
if side == 'black':
agent1, agent2 = agent2, agent1
GUI.run_gui(agent1, agent2, delay=0)
import agent
from util import printc, bcolors
if len(sys.argv) != 4:
print "Incorrect number of arguments. Please refer to the Readme file"
exit()
n = int(sys.argv[3])
depth = 2
width = 4
agents = [None, None]
if sys.argv[1] == "MCTreeSearch":
agents[0] = agent.MCTreeSearchAgent()
elif sys.argv[1] == "HumanAgent":
agents[0] = agent.HumanAgent(0)
elif sys.argv[1] == "PureMC":
agents[0] = agent.PureMCAgent()
elif sys.argv[1] == "Minimax":
agents[0] = agent.MinimaxAgent()
if sys.argv[2] == "MCTreeSearch":
agents[1] = agent.MCTreeSearchAgent()
elif sys.argv[2] == "HumanAgent":
agents[1] = agent.HumanAgent(1)
elif sys.argv[2] == "PureMC":
agents[1] = agent.PureMCAgent()
elif sys.argv[2] == "Minimax":
agents[1] = agent.MinimaxAgent()
#agent = [agent.MCTreeSearchAgent(agent.twoPinAwayPolicy, agent.twoPinAwayPolicy, 1000), agent.HumanAgent(1)]
def main(args=None):
from optparse import OptionParser
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
parser.add_option("-t",
"--train",
dest="train",
action="store_true",
default=False,
help="Train TD Player")
parser.add_option("-d",
"--draw",
dest="draw",
action="store_true",
default=False,
help="Draw game")
parser.add_option("-n",
"--num",
dest="numgames",
default=1,
help="Num games to play")
parser.add_option("-p",
"--player1",
dest="player1",
default="random",
help="Choose type of first player")
parser.add_option("-e",
"--eval",
dest="eval",
action="store_true",
default=False,
help="Play with the better eval function for player")
(opts, args) = parser.parse_args(args)
weights = None
if opts.train:
weights = train()
if opts.eval:
weights = load_weights(weights)
evalFn = submission.logLinearEvaluation
evalArgs = weights
else:
evalFn = submission.simpleEvaluation
evalArgs = None
p1 = None
if opts.player1 == 'random':
p1 = agent.RandomAgent(game.Game.TOKENS[0])
elif opts.player1 == 'reflex':
p1 = submission.ReflexAgent(game.Game.TOKENS[0], evalFn, evalArgs)
elif opts.player1 == 'expectimax':
p1 = submission.ExpectimaxAgent(game.Game.TOKENS[0], evalFn, evalArgs)
elif opts.player1 == 'human':
p1 = agent.HumanAgent(game.Game.TOKENS[0])
p2 = agent.RandomAgent(game.Game.TOKENS[1])
if p1 is None:
print "Please specify legitimate player"
import sys
sys.exit(1)
test([p1, p2], numGames=int(opts.numgames), draw=opts.draw)
def main(args=None):
import sys
print("Please choose the type of agent human or TDagent or random")
line = sys.stdin.readline()
from optparse import OptionParser
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
parser.add_option("-d",
"--draw",
dest="draw",
action="store_true",
default=False,
help="Draw game")
parser.add_option("-n",
"--num",
dest="numgames",
default=1,
help="Num games to play")
parser.add_option("-p",
"--player1",
dest="player1",
default=str(line.strip()),
help="Choose type of first player")
parser.add_option("-e",
"--eval",
dest="eval",
action="store_true",
default=True,
help="Play with the better eval function for player")
(opts, args) = parser.parse_args(args)
weights = None
weights1 = None
if opts.eval:
weights, weights1 = load_weights(weights, weights1)
evalArgs = weights
evalArgs1 = weights1
evalFn = aiAgents.nnetEval
print("The choosen agent is: " + str(opts.player1))
p1 = None
if str(opts.player1) == 'random':
p1 = agent.RandomAgent(game.Game.TOKENS[0])
#print p1
elif opts.player1 == 'TDagent':
p1 = aiAgents.TDAgent(game.Game.TOKENS[0], evalArgs1)
elif opts.player1 == 'expectimax':
p1 = aiAgents.ExpectimaxAgent(game.Game.TOKENS[0], evalFn, evalArgs)
elif opts.player1 == 'expectiminimax':
p1 = aiAgents.ExpectiMiniMaxAsgent(game.Game.TOKENS[0], evalFn,
evalArgs)
elif opts.player1 == 'human':
p1 = agent.HumanAgent(game.Game.TOKENS[0])
p2 = aiAgents.TDAgent(game.Game.TOKENS[1], evalArgs)
# p2 = aiAgents.ExpectiMiniMaxAgent(game.Game.TOKENS[1],evalFn,evalArgs)
if opts.player1 == 'random':
test([p1, p2], numGames=int(opts.numgames), draw=opts.draw)
print("o is random")
print("x is the agent")
if opts.player1 == 'TDagent':
#test([p1,p2],numGames=int(opts.numgames),draw=opts.draw)
play([p1, p2])
if opts.player1 == 'human':
play([p1, p2])
print("o is td(0)")
print("x is the agent td(0.5)")
if p1 is None:
print "Please specify legitimate player"
import sys
sys.exit(1)
def main(args=None):
# Parse command line arguments
argNames = [
'-trainGames', '-run', '-p', '-test', '-agent', '-opp', '-tournament',
'-human', '-verbose'
]
parser = argparse.ArgumentParser(description='Process input parameters.')
for arg in argNames:
parser.add_argument(arg)
namespace = parser.parse_args()
numPlayers = int(getattr(namespace, 'p')) if getattr(namespace, 'p') else 3
numTrainGames = int(getattr(namespace, 'trainGames')) if getattr(
namespace, 'trainGames') else 50
numTestGames = int(getattr(namespace, 'test')) if getattr(
namespace, 'test') else 100
numRuns = int(getattr(namespace, 'run')) if getattr(namespace,
'run') else 10
agentType = getattr(namespace, 'agent') if getattr(namespace,
'agent') else 'model'
oppType = getattr(namespace, 'opp') if getattr(namespace,
'opp') else 'simple'
isTournament = (getattr(namespace, 'tournament') == 'y')
isHuman = (getattr(namespace, 'human') == 'y')
verbose = (getattr(namespace, 'verbose') == 'y')
winRates = []
for i in range(numRuns):
# Play a game with human agent
if isHuman:
agents = [agent.HumanAgent(0)]
for j in range(1, numPlayers):
if oppType == 'random':
agents.append(agent.RandomAgent(j))
elif oppType == 'simple':
agents.append(
agent.ReflexAgent(j, evaluation.simpleEvaluation))
elif oppType == 'honest':
agents.append(agent.HonestAgent(j))
elif oppType == 'dishonest':
agents.append(agent.DishonestAgent(j))
elif oppType == 'bs':
agents.append(agent.AlwaysCallBSAgent(j))
g = game.Game(numPlayers, NUM_DECKS)
run_game(g, agents)
# Play a game with computer agent
else:
agents = []
if agentType == 'random':
agents.append(agent.RandomAgent(0))
elif agentType == 'simple':
agents.append(agent.ReflexAgent(0,
evaluation.simpleEvaluation))
elif agentType == 'honest':
agents.append(agent.HonestAgent(0))
elif agentType == 'dishonest':
agents.append(agent.DishonestAgent(0))
elif agentType == 'bs':
agents.append(agent.AlwaysCallBSAgent(0))
elif agentType == 'reflex':
if isTournament:
w = tournament(numPlayers, numTrainGames)
a = agent.ReflexAgent(0, logLinearEvaluation, w)
agents.append(a)
else:
w = train(numPlayers, numTrainGames)
a = agent.ReflexAgent(0, logLinearEvaluation, w)
agents.append(a)
elif agentType == 'model':
if isTournament:
w = tournament(numPlayers, numTrainGames, isModel=True)
a = agent.ModelReflexAgent(0, numPlayers,
logLinearEvaluation, w)
agents.append(a)
else:
w = train(numPlayers, numTrainGames, isModel=True)
a = agent.ModelReflexAgent(0, numPlayers,
logLinearEvaluation, w)
agents.append(a)
for j in range(1, numPlayers):
if oppType == 'random':
agents.append(agent.RandomAgent(j))
elif oppType == 'simple':
agents.append(
agent.ReflexAgent(j, evaluation.simpleEvaluation))
elif oppType == 'honest':
agents.append(agent.HonestAgent(j))
elif oppType == 'dishonest':
agents.append(agent.DishonestAgent(j))
elif oppType == 'bs':
agents.append(agent.AlwaysCallBSAgent(j))
winRates.append(test(agents, numTestGames, verbose))
if winRates:
avgWinRate = sum(winRates) / numRuns
print "Average win rate of agent is: {}".format(avgWinRate)
print("r was played in the game {} times".format(number_of_r))
dict_of_prs = {
"p": number_of_p,
"r": number_of_r,
"s": number_of_s
}
most_freq = max(dict_of_prs, key=dict_of_prs.get)
print('Most played move: {}'.format(most_freq))
print('=' * 20)
def compare(self, move_a1, move_a2):
if move_a1 == move_a2:
return 0
elif (move_a1 == "r" and move_a2 == "p"
or move_a1 == "p" and move_a2 == "s"
or move_a1 == "s" and move_a2 == "r"):
return 2
else:
return 1
human = agent.HumanAgent()
Computer = agent.InstructorAgent()
game = Game(human, Computer)
game.play(5)
game.summary()