def fitnessBattle(self, particle):
options = capture.readCommand(self.flags)
verbose = options.pop('verbose') # pop it off since runGame can't handle it
#options['display'] = None
options['numGames'] = 1
options['record'] = False
options['numTraining'] = False
for agent in options['agents']:
agent.setWeights(particle.position if agent.isRed else particle.bestKnown)
games = capture.runGames(**options)
assert(len(games) == 1)
if(games[0].state.data.score < 0): #Blue wins
return particle.bestKnownVal - 1
options = capture.readCommand(self.flags)
verbose = options.pop('verbose') # pop it off since runGame can't handle it
#options['display'] = None
options['numGames'] = 1
options['record'] = False
options['numTraining'] = False
for agent in options['agents']:
agent.setWeights(particle.bestKnown if agent.isRed else particle.position)
games = capture.runGames(**options)
assert(len(games) == 1)
if(games[0].state.data.score > 0): #Red wins
return particle.bestKnownVal - 1
bestSwarm = True
options = capture.readCommand(self.flags)
verbose = options.pop('verbose') # pop it off since runGame can't handle it
#options['display'] = None
options['numGames'] = 1
options['record'] = False
options['numTraining'] = 0
for agent in options['agents']:
agent.setWeights(particle.position if agent.isRed else particle.swarm.bestKnown)
games = capture.runGames(**options)
assert(len(games) == 1)
if(games[0].state.data.score < 0): #Blue wins
return particle.swarm.bestKnownVal
options = capture.readCommand(self.flags)
verbose = options.pop('verbose') # pop it off since runGame can't handle it
#options['display'] = None
options['numGames'] = 1
options['record'] = False
options['numTraining'] = 0
for agent in options['agents']:
agent.setWeights(particle.swarm.bestKnown if agent.isRed else particle.position)
games = capture.runGames(**options)
assert(len(games) == 1)
if(games[0].state.data.score > 0): #Red wins
return particle.swarm.bestKnownVal
return particle.swarm.bestKnownVal + 20
def eval_func(chromosome):
# arguments for the pacman game
argv = ["-r", "myTeam", "-b", "baselineTeam", "-l", "RANDOM107", "-Q", "-n", "3"]
options = readCommand(argv) # Get game components based on input
options["chromosome"] = chromosome.genomeList
games = runGames(**options)
# scores = [game.state.data.score - len(game.state.getBlueFood().asList()) + 2 for game in games];
# scores = [game.state.data.score + 100.0 * exp(-len(game.state.getBlueFood().asList()) + 2) for game in games];
# scores = [50.0 * exp(-len(game.state.getBlueFood().asList()) + 2) for game in games];
# foodEaten = [4 - len(game.state.getBlueFood().asList()) for game in games]
# foodLost = [4 - len(game.state.getRedFood().asList()) for game in games]
# pacmanKills = []
# scores = []
# for i in range(len(games)):
# killBalance = (games[i].state.data.score - (foodEaten[i] - foodLost[i]))
# pacmanKills.append(killBalance)
# # scores.append(foodEaten[i] - foodLost[i] + killBalance)
# scores.append(foodEaten[i] - (foodLost[i] * 10) + (killBalance / 10.0))
scores = [game.state.data.score for game in games]
minScore = min(scores)
avgScore = float(sum(scores)) / len(scores)
# print "Chromosome: ",
# print chromosome.genomeList,
# print ""
# print " got a score of: ",
# print avgScore
return avgScore
def eval_func():
# arguments for the pacman game
argv = ["-r", "myTeam", "-b", "baselineTeam", "-Q", "-n", "10"]
options = readCommand(argv) # Get game components based on input
games = runGames(**options)
scores = [game.state.data.score for game in games]
average = sum(scores) / 10.0
maxi = max(scores)
mini = min(scores)
return [average, maxi, mini]
optimizer = optim.Adam([A, b], lr=0.1)
for t in xrange(2000):
# Set the gradients to 0
optimizer.zero_grad()
# Compute the current predicted y's from x_dataset
y_predicted = model(x_dataset)
# See how far off the prediction is
current_loss = loss(y_predicted, y_dataset)
# Compute the gradient of the loss with respect to A and b
current_loss.backward()
# Update A and b accordingly
optimizer.step()
print "t = %s, loss = %s, A = %s, b = %s" % (t, current_loss,
A.detach().numpy(), b.item())
if __name__ == '__main__':
"""
The main function called when pacman.py is run
from the command line:
> python capture.py
See the usage string for more details.
> python capture.py --help
"""
options = readCommand(sys.argv[1:]) # Get game components based on input
games = runGames(**options)
def qFunc(state, weights):
s = np.asarray([state])
w = np.asarray([weights]).T
q = np.matmul(s, w)[0][0]
return q
if __name__ == '__main__':
for i in range(30):
with open("record.txt", "w") as f:
pass
argv = ["-r", "myTeam.py", "-b", "myTeam.py", "-l", "RANDOM", "-n", "2", "-q"]
options = readCommand(argv)
runGames(**options)
argv = ["-r", "myTeam.py", "-b", "shibaReflex.py", "-l", "RANDOM", "-n", "2", "-q"]
options = readCommand(argv)
runGames(**options)
argv = ["-r", "myTeam.py", "-b", "yellowdogReflex.py", "-l", "RANDOM", "-n", "2", "-q"]
options = readCommand(argv)
runGames(**options)
records = readRecords("record.txt")
with open("weights.txt", "r") as f:
weights = ast.literal_eval(f.read())
print(weights)
new_weights = train(10, records, weights)
print(new_weights)
with open("weights.txt", "w") as f:
f.write(json.dumps(new_weights))
with open("weights_history.txt", "a") as f:
def fitnessBattle(self, particle):
options = capture.readCommand(self.flags)
verbose = options.pop(
'verbose') # pop it off since runGame can't handle it
#options['display'] = None
options['numGames'] = 1
options['record'] = False
options['numTraining'] = False
for agent in options['agents']:
agent.setWeights(
particle.position if agent.isRed else particle.bestKnown)
games = capture.runGames(**options)
assert (len(games) == 1)
if (games[0].state.data.score < 0): #Blue wins
return particle.bestKnownVal - 1
options = capture.readCommand(self.flags)
verbose = options.pop(
'verbose') # pop it off since runGame can't handle it
#options['display'] = None
options['numGames'] = 1
options['record'] = False
options['numTraining'] = False
for agent in options['agents']:
agent.setWeights(
particle.bestKnown if agent.isRed else particle.position)
games = capture.runGames(**options)
assert (len(games) == 1)
if (games[0].state.data.score > 0): #Red wins
return particle.bestKnownVal - 1
bestSwarm = True
options = capture.readCommand(self.flags)
verbose = options.pop(
'verbose') # pop it off since runGame can't handle it
#options['display'] = None
options['numGames'] = 1
options['record'] = False
options['numTraining'] = 0
for agent in options['agents']:
agent.setWeights(
particle.position if agent.isRed else particle.swarm.bestKnown)
games = capture.runGames(**options)
assert (len(games) == 1)
if (games[0].state.data.score < 0): #Blue wins
return particle.swarm.bestKnownVal
options = capture.readCommand(self.flags)
verbose = options.pop(
'verbose') # pop it off since runGame can't handle it
#options['display'] = None
options['numGames'] = 1
options['record'] = False
options['numTraining'] = 0
for agent in options['agents']:
agent.setWeights(
particle.swarm.bestKnown if agent.isRed else particle.position)
games = capture.runGames(**options)
assert (len(games) == 1)
if (games[0].state.data.score > 0): #Red wins
return particle.swarm.bestKnownVal
return particle.swarm.bestKnownVal + 20
"""
Author: XuLin Yang
Student id: 904904
Date: 2020-2-6 23:31:18
Description: code to test the performance between each agents
"""
import time
import capture
"""
["D:\python\python.exe",
"-m",
"../capture.py",
"--delay 0.0",
"-r ./pacman_ai/myTeam.py --redOpts=first=SearchAgent,second=SearchAgent",
"-b ../baselineTeam.py --blueOpts=first=DefensiveReflexAgent,second=DefensiveReflexAgent"]
"""
if __name__ == "__main__":
start_time = time.time()
options = capture.readCommand([
"-r", "./teams/pacman_ai/myTeam.py",
"--redOpts=first=InferenceAgent,second=InferenceAgent", "-b",
"./baselineTeam.py",
"--blueOpts=first=DefensiveReflexAgent,second=DefensiveReflexAgent"
])
games = capture.runGames(**options)
print('\nTotal Time Game: %s' % round(time.time() - start_time, 0))
def runGame(self):
from capture import runGames, save_score
self.options['server'] = self.server
games = runGames(**self.options)
self.resetGames()
save_score(games[0])
def eval_func2(genome):
argv = ["-r", "baselineTeam", "-b", "baselineTeam", "-q"]
options = readCommand(argv) # Get game components based on input
runGames(**options)
return 0