def play(self, path):
runner = LocalPacmanGameRunner(layout_path=path,
random_ghosts=True,
show_window=True,
zoom_window=1.0,
frame_time=0.1,
timeout=-1000)
game = runner.run_game(self)
def playOneGame(layouts, numberOfGames, training, optilio, numTraining):
for layout in layouts:
with open("weights.txt") as f:
weights = [float(x) for x in f.readline().split()]
agent = ApproxAgent(train=training,
optilio=optilio,
numTraining=numTraining,
weights_values=weights)
runner = LocalPacmanGameRunner(layout, random_ghosts=False)
game = runner.run_game(agent)
def load_runners():
paths = ['pacman_layouts/' + f for f in os.listdir('pacman_layouts')]
random_ghosts = [False, True]
runners, names = [], []
for p in paths:
for rg in random_ghosts:
runner = LocalPacmanGameRunner(layout_path=p,
random_ghosts=rg,
frame_time=0)
runners.append(runner)
names.append(p.split('/')[1] + '__random_ghosts={}'.format(rg))
assert len(runners) == len(names)
return runners, names
def playGame(layouts, numberOfGames, training, optilio, numTraining):
for layout in layouts:
print(">>> ------------------------------ >>>")
print(layout)
with open("weights.txt") as f:
weights = [float(x) for x in f.readline().split()]
# weights = [0,0,0,0]
# weights = [-27.9914705800362, -3394.38754114026, 334.87185911475, -60.5944964389825]
# random
# 237.0996722631916 -4588.853575536811 245.5868697927386 -84.61209079023303
# zeros
# 131.6082538875898 -3662.5998496219695 293.8740546541827 -29.798118394469327
# 157.61426275835402 -3713.705833105122 -35.71944232589656 296.6912552309395
# again from above
# -14.760990254051723 -4567.035436781129 372.17484562084405 274.64338077262755
agent = ApproxAgent(train=training,
optilio=optilio,
numTraining=numTraining,
weights_values=weights)
runner = LocalPacmanGameRunner(layout, random_ghosts=False)
games = []
for i in range(numberOfGames):
game = runner.run_game(agent)
games.append(game)
scores = [game.state.getScore() for game in games]
results = np.array([game.state.isWin() for game in games])
print("Avg score >>: {:0.2f}".format(np.mean(scores)))
print("Median score: {:0.2f}".format(np.median(scores)))
print("Win Rate: {}/{} {:0.2f}".format(results.sum(),
len(results),
results.mean()))
if __name__ == "__main__":
args = parse_args()
# Fix the random seed
if args.seed is not None:
seeds = generate_deterministic_seeds(args.seed, args.num_games)
else:
seeds = None
# Choose a Pacman agent
AgentClass = load_agent(args.agent)
agent = AgentClass()
from misio.pacman.pacman import LocalPacmanGameRunner
runner = LocalPacmanGameRunner(layout_dir=args.layout,
random_ghosts=args.random_ghosts,
show_window=not args.no_graphics,
zoom_window=args.zoom,
frame_time=args.frame_time)
games = []
for i in tqdm.trange(args.num_games, leave=False):
if seeds is not None:
random.seed(seeds[i])
game = runner.run_game(agent)
games.append(game)
scores = [game.state.getScore() for game in games]
results = np.array([game.state.isWin() for game in games])
print("Avg score: {:0.2f}".format(np.mean(scores)))
print("Best score: {:0.2f}".format(max(scores)))
print("Median score: {:0.2f}".format(np.median(scores)))
print("Worst score: {:0.2f}".format(min(scores)))
args = parse_args()
# Fix the random seed
if args.seed is not None:
seeds = generate_deterministic_seeds(args.seed, args.num_games)
else:
seeds = None
# Choose a Pacman agent
AgentClass = load_agent(args.agent)
agent = AgentClass()
from misio.pacman.pacman import LocalPacmanGameRunner
runner = LocalPacmanGameRunner(layout_path=args.layout,
random_ghosts=not args.smart_ghosts,
show_window=not args.no_graphics,
zoom_window=args.zoom,
frame_time=args.frame_time,
timeout=-1000)
games = []
for i in tqdm.trange(args.num_games, leave=False):
if seeds is not None:
random.seed(seeds[i])
game = runner.run_game(agent)
games.append(game)
scores = [game.state.getScore() for game in games]
results = np.array([game.state.isWin() for game in games])
print("Avg score: {:0.2f}".format(np.mean(scores)))
print("Best score: {:0.2f}".format(max(scores)))
print("Median score: {:0.2f}".format(np.median(scores)))
print("Worst score: {:0.2f}".format(min(scores)))
import numpy as np
from lab6.qlearningAgents import PacmanQAgent
from misio.pacman.pacman import LocalPacmanGameRunner
if __name__ == "__main__":
agent = PacmanQAgent()
"*** DISABLE TRAINING HERE ***"
all_results = []
for (dirpath, dirnames, filenames) in walk("sample_solution/pacman_layouts"):
for layout in filenames:
runner = LocalPacmanGameRunner(layout_path=f"{dirpath}/{layout}",
random_ghosts=True,
show_window=False,
timeout=-1000)
games = []
for i in range(10):
game = runner.run_game(agent)
games.append(game)
scores = [game.state.getScore() for game in games]
results = np.array([game.state.isWin() for game in games])
all_results += list(results)
print(layout)
print(f"Avg score: {np.mean(scores):0.2f}")
print(f"Best score: {max(scores):0.2f}")
print(f"Median score: {np.median(scores):0.2f}")
args = parse_args()
# Fix the random seed
if args.seed is not None:
seeds = generate_deterministic_seeds(args.seed, args.num_games)
else:
seeds = None
# Choose a Pacman agent
AgentClass = load_agent(args.agent)
agent = AgentClass()
runner = LocalPacmanGameRunner(args.layout,
not args.smart_ghosts,
not args.no_graphics,
args.zoom,
args.frame_time)
games = []
for i in tqdm.trange(args.num_games, leave=False):
if seeds is not None:
random.seed(seeds[i])
game = runner.run_game(agent)
games.append(game)
scores = [game.state.getScore() for game in games]
results = np.array([game.state.isWin() for game in games])
print("Avg score: {:0.2f}".format(np.mean(scores)))
print("Best score: {:0.2f}".format(max(scores)))
print("Median score: {:0.2f}".format(np.median(scores)))
print("Worst score: {:0.2f}".format(min(scores)))