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]
def __init__(self, optimizableDelegate, numParticles, flags, fitnessFunction = None):
self.flags = flags
opts = capture.readCommand(flags)
self.verbose = opts['verbose']
self.optimizableDelegate = optimizableDelegate
self.numParticles = numParticles
if(fitnessFunction == None):
self.fitnessFunction = ParticleSwarmOptimizer.fitnessBattle
self.fitnessInitializer = lambda x: 0
else:
self.fitnessFunction = fitnessFunction
self.fitnessInitializer = fitnessFunction
self.swarm = ParticleSwarm(optimizableDelegate, numParticles, self.fitnessInitializer)
def __init__(self,
optimizableDelegate,
numParticles,
flags,
fitnessFunction=None):
self.flags = flags
opts = capture.readCommand(flags)
self.verbose = opts['verbose']
self.optimizableDelegate = optimizableDelegate
self.numParticles = numParticles
if (fitnessFunction == None):
self.fitnessFunction = ParticleSwarmOptimizer.fitnessBattle
self.fitnessInitializer = lambda x: 0
else:
self.fitnessFunction = fitnessFunction
self.fitnessInitializer = fitnessFunction
self.swarm = ParticleSwarm(optimizableDelegate, numParticles,
self.fitnessInitializer)
return particle.swarm.bestKnownVal
return particle.swarm.bestKnownVal + 20
if __name__ == '__main__':
"""
Run this like you were running capture.py at the command line
Important Flags to know:
-n 4 # run 4 iterations
-x 12 # with 12 particles
-v # in verbose mode
"""
flags = sys.argv[1:]
options = capture.readCommand(flags)
agents = options['agents']
assert(len(agents) > 0)
optimizableDelegate = agents[0]
numIterations = options['numGames']
numParticles = options['numTraining']
verbose = options.pop('verbose')
if(verbose):
print("Calling swarm optimizer with numIterations = " + str(numIterations) + " numParticles = " + str(numParticles))
optimizer = ParticleSwarmOptimizer(optimizableDelegate, numParticles, flags)
bestValues = optimizer.optimize(numIterations)
print("Best Values = ")
print(bestValues)
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)
return ret
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))
line = f.readline()
self.learn_step_counter = int(line)
line = f.readline()
self.memory_counter = int(line)
f.close()
if __name__ == "__main__":
for i_episode in range(TRAINING_SIZE):
print "PLAYING TRAINING GAME " + str(i_episode) + ":"
arguments = [
"-l", "RANDOM", "-r", "kizunaTeam", "-b", "baselineTeam", "-q",
"--time", "80000"
]
options = readCommand(arguments)
games = runGames(**options)
print "\n\n"
EPSILON = 1.0
for i_episode in range(TEST_SIZE):
print "PLAYING TEST GAME " + str(i_episode) + ":"
arguments = ["-l", "RANDOM", "-r", "kizunaTeam", "-b", "baselineTeam"]
options = readCommand(arguments)
games = runGames(**options)
print "\n\n"
# print('\nCollecting experience...')
# for i_episode in range(TRAINING_SIZE):
#
# s = env.reset()
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
return particle.swarm.bestKnownVal
return particle.swarm.bestKnownVal + 20
if __name__ == '__main__':
"""
Run this like you were running capture.py at the command line
Important Flags to know:
-n 4 # run 4 iterations
-x 12 # with 12 particles
-v # in verbose mode
"""
flags = sys.argv[1:]
options = capture.readCommand(flags)
agents = options['agents']
assert (len(agents) > 0)
optimizableDelegate = agents[0]
numIterations = options['numGames']
numParticles = options['numTraining']
verbose = options.pop('verbose')
if (verbose):
print("Calling swarm optimizer with numIterations = " +
str(numIterations) + " numParticles = " + str(numParticles))
optimizer = ParticleSwarmOptimizer(optimizableDelegate, numParticles,
flags)
bestValues = optimizer.optimize(numIterations)
print("Best Values = ")
print(bestValues)
"""
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 eval_func2(genome):
argv = ["-r", "baselineTeam", "-b", "baselineTeam", "-q"]
options = readCommand(argv) # Get game components based on input
runGames(**options)
return 0
