def test_getStateVars2(self):
simulatedError = 0.01
simWidth = 550.0
simHeight = 357.0
ballPos = (1/3 * simWidth,1/6 * simHeight)
c = (simWidth /2.0,simHeight/2.0)
a1 = agent.agent((1/3 * simWidth,1/6 * simHeight),simulatedError,"Keeper",ballPos)
a2 = agent.agent((2/3 * simWidth,1/7 * simHeight),simulatedError,"Keeper",ballPos)
a3 = agent.agent((2/5 * simWidth,6/7 * simHeight),simulatedError,"Keeper",ballPos)
keepers = [a1,a2,a3]
t1 = agent.agent((1/2 * simWidth,5/12 * simHeight),simulatedError,"Taker",ballPos)
t2 = agent.agent((2/5 * simWidth,7/12 * simHeight),simulatedError,"Taker",ballPos)
takers = [t1,t2]
testOut = getStateVarsKeepers(keepers, takers, c)
actualOut = [kUtil.getDist((550/3, 59.5), c),
kUtil.getDist((550/3 * 2, 51), c),
kUtil.getDist((220, 306), c),
kUtil.getDist((275, 148.75), c),
kUtil.getDist((220, 208.25), c),
kUtil.getDist((550/3,59.5), (550/3*2, 51)),
kUtil.getDist((550/3,59.5), (220,306)),
kUtil.getDist((550/3,59.5), (275, 148.75)),
kUtil.getDist((550/3,59.5), (220, 208.25)),
min( kUtil.getDist((550/3*2,51), (220, 208.25)), kUtil.getDist((550/3*2,51), (275, 148.75)) ),
min( kUtil.getDist((220,306), (220, 208.25)), kUtil.getDist((220,306), (275, 148.75)) ),
max(kUtil.cosTheta((550/3*2, 51), (550/3,59.5), (275,148.75)),
kUtil.cosTheta((550/3*2, 51), (550/3,59.5), (220,208.25))),
max(kUtil.cosTheta((220,306), (550/3,59.5), (275,148.75)),
kUtil.cosTheta((220,306), (550/3,59.5), (220,208.25))),
]
for i in range(len(testOut)):
self.assertAlmostEqual(testOut[i], actualOut[i], 1,"Failed on index: %d" % i)
def test_getStateVars(self):
# self, worldRef, pos, sigma, agentType, trueBallPos, maxPlayerSpeed, maxBallSpeed, inPossession = False
import keepAway
keepAwayWorld = keepAway.keepAway()
ballPos = (0, 0)
center = (0, 0)
simulatedError = 0.01
a1 = agent.agent(
keepAwayWorld,
(10, 0),
simulatedError,
"Keeper",
ballPos,
keepAwayWorld.maxPlayerSpeed,
keepAwayWorld.maxBallSpeed,
)
a2 = agent.agent(
keepAwayWorld,
(0, 0),
simulatedError,
"Keeper",
ballPos,
keepAwayWorld.maxPlayerSpeed,
keepAwayWorld.maxBallSpeed,
)
a3 = agent.agent(
keepAwayWorld,
(0, 5),
simulatedError,
"Keeper",
ballPos,
keepAwayWorld.maxPlayerSpeed,
keepAwayWorld.maxBallSpeed,
)
keepers = [a1, a2, a3]
t1 = agent.agent(
keepAwayWorld,
(5, 5),
simulatedError,
"Taker",
ballPos,
keepAwayWorld.maxPlayerSpeed,
keepAwayWorld.maxBallSpeed,
)
t2 = agent.agent(
keepAwayWorld,
(5, 0),
simulatedError,
"Taker",
ballPos,
keepAwayWorld.maxPlayerSpeed,
keepAwayWorld.maxBallSpeed,
)
takers = [t1, t2]
testOut = getStateVarsKeepers(keepers, takers, center)
actualOut = [0, 5, 10, 5, math.sqrt(50), 5, 10, 5, math.sqrt(50), 5, 5, math.cos(math.pi / 4.0), 1]
for i in range(len(testOut)):
self.assertAlmostEqual(testOut[i], actualOut[i], 1)
def __init__(self):
mode = None #will be set to either monte carlo, q learning, sarsa, or manual control in the intro
#RGB color
self.white = (255,255,255)
self.black = (0,0,0)
self.red = (255,0,0)
self.green = (0,155,0)
self.blue = (0,0,255)
#give the game a title
pygame.display.set_caption('Keepaway')
self.keeperScore = 0
self.displayGraphics = True
#these are more or less global variables..
#I'm not sure if this is bad or not.
self.worldImage = pygame.image.load('images/soccer_field.png')
self.ballImage = pygame.image.load('images/ball.png')
self.keeperImage = pygame.image.load('images/keeper.png')
self.takerImage = pygame.image.load('images/taker.png')
#block sizes are used for collision detection
#only 1 size per element because all blocks are squares. block size = side length
self.agent_block_size = 23
self.ball_block_size = 12
self.maxBallSpeed= 3
self.maxPlayerSpeed = 2
#dimensions of the game are the same as the soccer field image
self.display_width = 550
self.display_height = 357
self.field_center = (self.display_width / 2 , self.display_height / 2)
#gameDisplay is a pygame.surface object. it's your screen
self.gameDisplay = pygame.display.set_mode((self.display_width,self.display_height))
self.fps = 60
self.clock = pygame.time.Clock()
types = ["keeper", "taker"]
agentSigmaError = .01
#start the ball kinda close to the keeper in the upper left corner
self.fieldBall = ball.ball( (self.field_center[0]/4, self.field_center[1]/4), self.maxBallSpeed)
#setup all the initial keepers and takers. They are all starting at different field positions, which is why
#you can't have a for loop just iterate and declare all of them
self.keeperArray = []
self.keeperArray.append(agent.agent(self, (12.5, 12.5), agentSigmaError, types[0], self.field_center, self.maxPlayerSpeed, self.maxBallSpeed))
self.keeperArray.append(agent.agent(self, (25, self.display_width - 37.5), agentSigmaError, types[0], self.field_center, self.maxPlayerSpeed, self.maxBallSpeed))
self.keeperArray.append(agent.agent(self, (self.display_height - 37.5, self.display_width - 37.5), agentSigmaError, types[0], self.field_center, self.maxPlayerSpeed, self.maxBallSpeed))
self.takerArray = []
self.takerArray.append(agent.agent(self, (self.display_height - 25, 25), agentSigmaError, types[1], self.field_center, self.maxPlayerSpeed, self.maxBallSpeed))
self.takerArray.append(agent.agent(self, (self.display_height - 37.5, 50), agentSigmaError, types[1], self.field_center, self.maxPlayerSpeed, self.maxBallSpeed))
#3 different font sizes
self.smallfont = pygame.font.SysFont("comicsansms",25) #25 is font sizes
self.medfont = pygame.font.SysFont("comicsansms",50)
self.largefont = pygame.font.SysFont("comicsansms",80)
self.verysmallfont = pygame.font.SysFont("comicsansms", 12)
def test_getStateVars(self):
ballPos = (0,0)
center = (0,0)
simulatedError = 0.01
a1 = agent.agent((10,0),simulatedError,"Keeper",ballPos)
a2 = agent.agent((0,0),simulatedError,"Keeper",ballPos)
a3 = agent.agent((0,5),simulatedError,"Keeper",ballPos)
keepers = [a1,a2,a3]
t1 = agent.agent((5,5),simulatedError,"Taker",ballPos)
t2 = agent.agent((5,0),simulatedError,"Taker",ballPos)
takers = [t1,t2]
testOut = getStateVarsKeepers(keepers, takers, center)
actualOut = [0,5,10,5,math.sqrt(50),5,10,5,math.sqrt(50),5,5,math.cos(math.pi / 4.0),1]
for i in range(len(testOut)):
self.assertAlmostEqual(testOut[i], actualOut[i], 1)
def start_from_terminal(app):
parser = optparse.OptionParser()
parser.add_option("-p", "--port", help="which port to serve content on", type="int", default=port)
opts, args = parser.parse_args()
net_args = {
"model_def_file": model_def_file,
"pretrained_model_file": pretrained_model_file,
"gpu_mode": gpu_mode,
"device_id": 1,
"image_dim": image_dim,
"raw_scale": raw_scale,
}
# Initialize classifier
app.agent = agent(**net_args)
logging.info("Initialize vision model done")
# warm start by forward for allocation
app.agent.net.forward()
logging.info("Net forward done")
app.indexer = indexer()
for category_id in CATEGORY_NAME:
app.indexer.load(category_id, DATABASE_FILENAME % category_id)
logging.info("Loading indexer for {}".format(category_id))
logging.info("Initialize indexer done")
# app.indexer.load(DATABASE_FILE)
start_tornado(app, opts.port)
def test_distCenter(self):
a1 = agent.agent((0, 0), self.unitTestSigma, "Keeper", (0, 0))
self.assertAlmostEqual(__distCenter(a1, (0, 0)), 0, 1)
self.assertAlmostEqual(__distCenter(a1, (0, 10)), 10, 1)
self.assertAlmostEqual(__distCenter(a1, (10, 0)), 10, 1)
self.assertAlmostEqual(__distCenter(a1, (1, 1)), math.sqrt(2), 1)
self.assertAlmostEqual(__distCenter(a1, (10, 10)), math.sqrt(200), 1)
def getListOfAgents(version): worldFile = getWorldFile(version) if(worldFile == 0): return [] listOfAgents = [] for line in worldFile.readlines(): a = agent.agent(line) listOfAgents.append(a) return listOfAgents
def setup():
size(768, 768)
frameRate(fps)
global g
g = grid.generate()
global a
a = agent()
def fuzz (self, this_node=None, path=[]):
'''
Call this routine to get the ball rolling. No arguments are necessary as they are
both utilized internally during the recursive traversal of the session graph.
@type this_node: request (node)
@param this_node: (Optional, def=None) Current node that is being fuzzed.
@type path: List
@param path: (Optional, def=[]) Nodes along the path to the current one.
'''
# if no node is specified, we start from root and initialize the session.
if not this_node:
# we can't fuzz if we don't have at least one target and one request.
if not self.target:
syslog.syslog(syslog.LOG_ERR, self.session_id +
": no target specified for session")
return
if not self.edges_from(self.root.id):
syslog.syslog(syslog.LOG_ERR, self.session_id +
": no request specified for session")
return
this_node = self.root
self.total_mutant_index = 0
self.total_num_mutations = self.num_mutations()
# If no errors above and not already connected to the agent, initialize the
# agent connection.
# If the agent cannot be initialized make sure the user is aware of it.
if self.agent == None and self.agent_settings != None:
try:
self.agent = agent(self.config, self.session_id, self.agent_settings)
self.agent.connect()
except Exception, ex:
syslog.syslog(syslog.LOG_ERR, self.session_id +
": failed to establish agent connection (%s)" % str(ex))
self.finished_flag = True
self.stop_flag = True
return
# Get the agent to execute
try:
self.agent.start()
except Exception, ex:
syslog.syslog(syslog.LOG_ERR, self.session_id +
": agent failed to execute command (%s)" % str(ex))
self.finished_flag = True
self.stop_flag = True
return
def initialize( self ):
self.inds = []
delprn( "Creating Trees\t\t", 2 )
#Set up random trees
for i in range(0,self.mu):
delprn( str(perStr( i/self.mu )), 3 )
self.inds.append(agent( self ) )
delprn( "Calc. Inital Fitness\t", 2 )
#Do our initial run
for i in range(0,len(self.inds)):
delprn( str(perStr( i/len(self.inds) )), 3 )
self.inds[i].fitness( )
def recombination( self ):
parents = self.parentSelection( )
kids = []
delprn( "Creating Children\t", 2 )
for i in range(0,len(parents)):
delprn( str(perStr( i/self.lamb )), 3 )
pair = parents[i]
p1 = pair[0]
p2 = pair[1]
#We're just doing cross over for now, so hardcode this in:
#Create two kids, one from each parent
kid1 = agent( self, copy=p1 )
kid2 = agent( self, copy=p2 )
#And sample for a random crossover point from both kids
kid1pt = random.sample(kid1.tree.nodes, 1)[0]
kid2pt = random.sample(kid2.tree.nodes, 1)[0]
#Now swap subtrees
tree.swapsubtree( kid1.tree, kid1pt, kid2.tree, kid2pt )
#Mutate them
kid1.mutate( )
kid2.mutate( )
kids.append(kid1)
kids.append(kid2)
if self.strat == PLUS:
for ind in kids:
self.inds.append( ind )
elif self.strat == COMMA:
for ind in self.inds:
ind.delete( )
self.inds = kids
def __init__(self, port):
util_root = '/works/demon_11st/utils'
sys.path.insert(0, util_root)
from exifutil import exifutil
app.exifutils = exifutil()
#import pdb; pdb.set_trace()
agent_root = '/works/demon_11st/agent/detection'
sys.path.insert(0, agent_root)
import _init_paths
from conf import conf
from agent import agent
yaml_file = '/storage/product/detection/11st_All/cfg/faster_rcnn_end2end_test.yml'
conf = conf(yaml_file, 0)
app.agent = agent()
from korean_url_handler import korean_url_handler
app.korean_url_handler = korean_url_handler()
# start web server
web_server.__init__(self, app, port)
def __init__(self,
port,
net_args, oversample,
category_no, max_num_items, database_filename):
self.net_args = net_args
self.database_filename = database_filename
# Initialize classifier
app.oversample = oversample
app.agent = agent(**self.net_args)
logging.info('Initialize vision model done')
app.agent.net.forward()
logging.info('Net forward done')
# Initialize indexer
app.indexer = indexer(category_no, max_num_items)
app.indexer.load_category(database_filename)
logging.info('Initialize indexer done')
# get parser_utils
app.parser_utils = parser_utils()
app.korean_url_handler = korean_url_handler()
# start web server
web_server.__init__(self, app, port)
def absBestFinish( self, cfg, best ):
self.res.write( "\nTree with the Global Best Fitness\n" )
#Mock container generation
generation = gen( cfg )
#Avoiding errors
best.gen = generation
self.res.write( "\nRandom GP Performance\n" )
self.res.write( "Global best's gen #: " + str(best.gennum) + "\n" )
#Clear old payoffs
best.payoffs = []
#Randomly make many individuals to face.
for i in range(30):
generation.inds.append( agent( generation ) )
for opp in generation.inds:
beforepayoff = best.mem*2
for j in range(0,generation.seqs):
tmoves = opp.mymoves
oppres = opp.run( best.mymoves )
myres = best.run( opp.mymoves )
if j > beforepayoff:
best.upres( myres, oppres )
opp.upres( oppres, myres )
avg = 0
for i in best.payoffs:
avg += i
avg /= len(best.payoffs)
self.res.write( "Random fit: " + str(avg) + "\n" )
self.csv.write( "\n\n" + "Global Best Gen #,avgabsfit,lastwinfit,csv,random fit" + "\n" )
self.csv.write( str(best.gennum) + "," + str(best.fit) + "," + str(best.fits[0]) + "," + str(best.fits[1]) + "," + str(avg) + "\n" )
def __init__(self, _num_adv, _num_agents, _num_drones):
g_var.num_of_adverseries = _num_adv
g_var.num_of_agents = _num_agents
g_var.num_of_drones = _num_drones
# re-initialization of result variables
g_var.arrested_poachers = 0
g_var.fled_poachers = 0
g_var.resource_poached = 0
g_var.resource_recovered = 0
g_var.distance_travelled = 0
self.refresh_counter = 0
self.refresh_limit = 40
print "Parameters: adversaries: " + str(g_var.num_of_adverseries),\
", agents: " + str(g_var.num_of_agents),\
", drones: " + str(g_var.num_of_drones)
self.root = Tk()
self.root.title("Green Security Game")
self.root.geometry('640x480+620+120')
self.canvas = Canvas(self.root, bg="#333333", height=480, width=640)
self.canvas.pack()
Frame.__init__(self)
self.agent_pos = [[0 for i in range(g_var.dimension)]
for j in range(g_var.dimension)]
#self.cell_resources = [[random.randint(10,50) for i in range(global_var.dimension)] for j in range(global_var.dimension)]
self.adv_pos = [[0 for i in range(g_var.dimension)]
for j in range(g_var.dimension)]
self.drone_pos = [[0 for i in range(g_var.dimension)]
for j in range(g_var.dimension)]
self.drone_signal = [[0 for i in range(g_var.dimension)]
for j in range(g_var.dimension)]
self.target_pos = []
self.round_marking = []
self.cell_resources = [[4, 9, 6, 7, 0, 2, 1, 6, 7, 0],
[13, 50, 0, 0, 50, 0, 50, 0, 0, 21],
[14, 0, 19, 13, 24, 23, 36, 17, 0, 11],
[17, 50, 40, 10, 50, 50, 50, 6, 0, 6],
[10, 31, 20, 13, 50, 0, 0, 10, 50, 3],
[9, 34, 30, 10, 50, 50, 50, 10, 0, 5],
[11, 37, 10, 22, 17, 15, 12, 10, 0, 6],
[13, 0, 50, 14, 33, 17, 50, 32, 26, 11],
[7, 0, 0, 50, 0, 0, 0, 50, 13, 23],
[11, 12, 31, 10, 9, 8, 11, 13, 14, 21]]
for i in range(g_var.dimension):
for j in range(g_var.dimension):
if self.cell_resources[i][j] > 0:
self.target_pos.append((i, j))
if self.cell_resources[i][j] == -1:
self.round_marking.append((i, j))
self.round_marking.append((5, 5)) # temporary dummy
self.cell_coord = [[
i.__str__() + "," + j.__str__() for i in range(g_var.dimension)
] for j in range(g_var.dimension)]
self.label_poacher_num = Label(self.root,
text="Number of Total \nPoachers:\n" +
str(g_var.num_of_adverseries))
self.label_poacher_num.place(relx=0.78, rely=0.05)
self.label_arrest = Label(self.root, text=g_var.arrested_poachers)
self.label_arrest.place(relx=0.78, rely=0.2)
self.label_fled = Label(self.root, text=g_var.fled_poachers)
self.label_fled.place(relx=0.78, rely=0.3)
self.label_sack = Label(self.root, text=g_var.resource_poached)
self.label_sack.place(relx=0.78, rely=0.4)
self.label_recovered = Label(self.root, text=g_var.resource_poached)
self.label_recovered.place(relx=0.78, rely=0.5)
self.label_travelled = Label(self.root, text=g_var.distance_travelled)
self.label_travelled.place(relx=0.78, rely=0.6)
self.label_agent_num = Label(self.root,
text="Number of Agents:\n" +
str(g_var.num_of_agents))
self.label_agent_num.place(relx=0.78, rely=0.7)
self.label_drone_num = Label(self.root,
text="Number of Drones:\n" +
str(g_var.num_of_drones))
self.label_drone_num.place(relx=0.78, rely=0.8)
self.refresh()
self.canvas.create_rectangle(0,
0,
g_var.dimension * g_var.block_size,
g_var.dimension * g_var.block_size,
fill=g_var.bg_color)
# for ONE TIME labelling *******************************************
for i in range(g_var.dimension):
for j in range(g_var.dimension):
self.coord_label = Label(self.root,
text=self.cell_coord[i][j],
bg="black",
fg="white")
self.coord_label.place(x=i * g_var.block_size + 2,
y=j * g_var.block_size + 18)
for i in range(g_var.dimension + 1):
for j in range(g_var.dimension + 1):
self.canvas.create_rectangle(i * g_var.block_size,
j * g_var.block_size,
g_var.block_size,
g_var.block_size,
outline="grey")
for i in range(g_var.num_of_agents):
agent_obj = agent(self.canvas, self.root, self.agent_pos,
self.cell_resources, self.target_pos,
self.round_marking, self.drone_signal)
agent_obj.move_spec_guard()
for i in range(g_var.num_of_drones):
drone_obj = drone(self.canvas, self.root, self.drone_pos,
self.drone_signal, self.adv_pos)
drone_obj.move_drone()
for i in range(g_var.num_of_adverseries):
adv_obj = adv(self.canvas, self.root, self.agent_pos,
self.drone_pos, self.cell_resources, self.target_pos,
self.adv_pos)
adv_obj.operate_adv()
self.root.mainloop()
from simulGivenRoutes import execute
from getRoutes import getRoutes
from itertools import product
from copy import deepcopy
from ARoptimization import ARoptim
from ExpOptim import updatePosterior
from ExpOptim import generateDraws
#network creation
network=[ [None] * 4 for i in range(4)]
network[0][1]=edge.edge('x')
network[1][2]=edge.edge('x')
network[1][3]=edge.edge('x')
network[3][2]=edge.edge('x')
#agent creation
agents=list()
agents.append(agent.agent(0,2,1))
agents.append(agent.agent(1,2,2))
agents.append(agent.agent(1,2,3))
agents.append(agent.agent(1,2,1))
#%%
#Experience
#sensitivity to New information
delta=0.1
#getting routes and costs
routesAndCosts=getRoutes(network, agents, 10, cost=True)
#calculate the first probability distribution
posterior=updatePosterior(routesAndCosts['costs'])
prior=posterior
#get a route draw for each agent
while True:
for i in range(3000):
f, ax = plt.subplots(1, 1)
ax.plot(reward_ts)
plt.show()
else:
if args.weight_sharing:
model = rnn.RDQN_multi(args)
target = rnn.RDQN_multi(args)
optimizer = optim.RMSprop(model.parameters(), lr=args.learning_rate, momentum=args.learning_momentum)
game = env.env_multi(args)
players = []
for i in range(args.n_agents):
player = agent.agent(args, agent_id=i)
if args.weight_sharing:
player.setup_models(model, target)
else:
model = rnn.RDQN_multi(args)
target = rnn.RDQN_multi(args)
optimizer = optim.RMSprop(model.parameters(), lr=args.learning_rate, momentum=args.learning_momentum)
player.setup_models(model, target)
player.set_optimizer(optimizer)
players.append(player)
criterion = nn.MSELoss()
average_loss = 0
average_reward = 0
loss_record = []
class Matris(object):
board = agent.board()
agent_mode = True #used to check if agent is playing. Causes hard-drops to always happen.
if agent_mode == True:
if (sys.argv[1] == "-hh"):
#Creates an agent that takes column differences, holes and height of the tallest column as inputs
agent = agent.agent([],
int(sys.argv[2]),
random_moves=False,
rewards_as_lines=True,
epsilon=1,
epsilon_decay=0.01,
epsilon_minimum=0.01,
memory_size=1000,
sample_size=32,
reset_steps=1000,
height=True,
holes=True)
elif (sys.argv[1] == "-ho"):
#Creates an agent that takes column differences and holes as inputs
agent = agent.agent([],
int(sys.argv[2]),
random_moves=False,
rewards_as_lines=True,
epsilon=1,
epsilon_decay=0.01,
epsilon_minimum=0.01,
memory_size=1000,
sample_size=32,
reset_steps=1000,
holes=True)
elif (sys.argv[1] == "-hi"):
#Creates an agent that takes column differences and height of the tallest column as inputs
agent = agent.agent([],
int(sys.argv[2]),
random_moves=False,
rewards_as_lines=True,
epsilon=1,
epsilon_decay=0.01,
epsilon_minimum=0.01,
memory_size=1000,
sample_size=32,
reset_steps=1000,
height=True)
elif (sys.argv[1] == "-no"):
#Creates an agent that takes column differences as inputs only
agent = agent.agent([],
int(sys.argv[2]),
random_moves=False,
rewards_as_lines=True,
epsilon=1,
epsilon_decay=0.01,
epsilon_minimum=0.01,
memory_size=1000,
sample_size=32,
reset_steps=1000)
elif (sys.argv[1] == "-ra"):
#Creates an agent that plays randomly
agent = agent.agent([], int(sys.argv[2]), random_moves=True)
elif (sys.argv[1] == "-lo"):
#Loads an agent that has previously been trained in MaTris. Loads .obj file.
agent = agent.agent([],
int(sys.argv[2]),
random_moves=False,
rewards_as_lines=True,
epsilon=1,
epsilon_decay=0.01,
epsilon_minimum=0.01,
memory_size=1000,
sample_size=32,
reset_steps=1000,
filepath=sys.argv[3])
elif (sys.argv[1] == "-lt"):
#Loads an agent that has previously been trained using supervised learning in MaTris-O. Loads .obj file.
agent = agent.agent([],
int(sys.argv[2]),
random_moves=False,
rewards_as_lines=True,
epsilon=1,
epsilon_decay=0.01,
epsilon_minimum=0.01,
memory_size=1000,
sample_size=32,
reset_steps=1000,
filepath=sys.argv[3],
supervised=True)
else:
raise Exception(
error_message=
"\n\nError inputting command line arguments\nUsage:\n[mode] [number of episodes]\nmode:\n\t-hh - holes and height and column differences\n\t-ho - holes and column differences\n\t-hi - height and column differences\n\t-no - column differences only\n\tLoad ANN\nSecond argument should be number of episodes\n third argument should be filepath if file is being loaded."
)
seed = agent.load_new_seed()
random.seed(seed)
tetromino_placement = None
def __init__(self):
self.surface = screen.subsurface(
Rect((MATRIS_OFFSET + BORDERWIDTH, MATRIS_OFFSET + BORDERWIDTH),
(MATRIX_WIDTH * BLOCKSIZE, (MATRIX_HEIGHT - 2) * BLOCKSIZE)))
self.matrix = dict()
for y in range(MATRIX_HEIGHT):
for x in range(MATRIX_WIDTH):
self.matrix[(y, x)] = None
"""
`self.matrix` is the current state of the tetris board, that is, it records which squares are
currently occupied. It does not include the falling tetromino. The information relating to the
falling tetromino is managed by `self.set_tetrominoes` instead. When the falling tetromino "dies",
it will be placed in `self.matrix`.
"""
self.next_tetromino = random.choice(list_of_tetrominoes)
self.set_tetrominoes()
if self.agent_mode == True:
#Creates a representation of the initial board
self.board.update_board_representation(
self.create_board_representation())
self.board.set_board_height()
self.board.set_holes()
self.board.set_column_differences()
print(str(self.board))
print("Column Height Differences:" +
str(self.board.get_column_differences()))
#Set up the the agent
self.agent.set_current_board(self.board)
self.agent.set_agent_tetromino(self.current_tetromino)
self.tetromino_rotation = 0
self.downwards_timer = 0
self.base_downwards_speed = 0.4 # Move down every 400 ms
self.movement_keys = {'left': 0, 'right': 0}
self.movement_keys_speed = 0.05
self.movement_keys_timer = (-self.movement_keys_speed) * 2
self.level = 1
self.score = 0
self.lines = 0
self.combo = 1 # Combo will increase when you clear lines with several tetrominos in a row
self.paused = False
self.highscore = load_score()
self.played_highscorebeaten_sound = False
self.levelup_sound = get_sound("levelup.wav")
self.gameover_sound = get_sound("gameover.wav")
self.linescleared_sound = get_sound("linecleared.wav")
self.highscorebeaten_sound = get_sound("highscorebeaten.wav")
if self.agent_mode == True:
#Agent's first move
self.tetromino_placement = self.agent.make_move()
self.tetromino_position = (0, self.tetromino_placement[2])
for rotations in range(self.tetromino_placement[0]):
self.request_rotation()
def set_tetrominoes(self):
"""
Sets information for the current and next tetrominos
"""
self.current_tetromino = self.next_tetromino
self.next_tetromino = random.choice(list_of_tetrominoes)
self.surface_of_next_tetromino = self.construct_surface_of_next_tetromino(
)
self.tetromino_position = (0, 4) if len(
self.current_tetromino.shape) == 2 else (0, 3)
self.tetromino_rotation = 0
self.tetromino_block = self.block(self.current_tetromino.color)
self.shadow_block = self.block(self.current_tetromino.color,
shadow=True)
def hard_drop(self):
"""
Instantly places tetrominos in the cells below
"""
amount = 0
while self.request_movement('down'):
amount += 1
self.score += 10 * amount
self.lock_tetromino()
def update(self, timepassed):
"""
Main game loop
"""
try:
self.needs_redraw = False
if self.agent_mode == True:
self.hard_drop()
else:
#Handles player input
pressed = lambda key: event.type == pygame.KEYDOWN and event.key == key
unpressed = lambda key: event.type == pygame.KEYUP and event.key == key
events = pygame.event.get()
#Controls pausing and quitting the game.
for event in events:
if pressed(pygame.K_p):
self.surface.fill((0, 0, 0))
self.needs_redraw = True
self.paused = not self.paused
elif event.type == pygame.QUIT:
self.gameover(full_exit=True)
elif pressed(pygame.K_ESCAPE):
self.gameover()
if self.paused:
return self.needs_redraw
for event in events:
#Handles player input
#Controls movement of the tetromino
if pressed(pygame.K_SPACE):
self.hard_drop()
elif pressed(pygame.K_UP) or pressed(pygame.K_w):
self.request_rotation()
elif pressed(pygame.K_LEFT) or pressed(pygame.K_a):
self.request_movement('left')
self.movement_keys['left'] = 1
elif pressed(pygame.K_RIGHT) or pressed(pygame.K_d):
self.request_movement('right')
self.movement_keys['right'] = 1
elif unpressed(pygame.K_LEFT) or unpressed(pygame.K_a):
self.movement_keys['left'] = 0
self.movement_keys_timer = (
-self.movement_keys_speed) * 2
elif unpressed(pygame.K_RIGHT) or unpressed(pygame.K_d):
self.movement_keys['right'] = 0
self.movement_keys_timer = (
-self.movement_keys_speed) * 2
self.downwards_speed = self.base_downwards_speed**(
1 + self.level / 10.)
self.downwards_timer += timepassed
downwards_speed = self.downwards_speed * 0.10 if any([
pygame.key.get_pressed()[pygame.K_DOWN],
pygame.key.get_pressed()[pygame.K_s]
]) else self.downwards_speed
if self.downwards_timer > downwards_speed:
if not self.request_movement(
'down'
): #Places tetromino if it cannot move further down
self.lock_tetromino()
self.downwards_timer %= downwards_speed
if any(self.movement_keys.values()):
self.movement_keys_timer += timepassed
if self.movement_keys_timer > self.movement_keys_speed:
self.request_movement(
'right' if self.movement_keys['right'] else 'left')
self.movement_keys_timer %= self.movement_keys_speed
except:
print("Error in agent running")
print(
"Manually causing gameover. Preserves continuation of agent running with minor potential impediment on learning."
)
self.gameover()
self.needs_redraw = True
return self.needs_redraw
def draw_surface(self):
"""
Draws the image of the current tetromino
"""
with_tetromino = self.blend(matrix=self.place_shadow())
for y in range(MATRIX_HEIGHT):
for x in range(MATRIX_WIDTH):
# I hide the 2 first rows by drawing them outside of the surface
block_location = Rect(x * BLOCKSIZE,
(y * BLOCKSIZE - 2 * BLOCKSIZE),
BLOCKSIZE, BLOCKSIZE)
if with_tetromino[(y, x)] is None:
self.surface.fill(BGCOLOR, block_location)
else:
if with_tetromino[(y, x)][0] == 'shadow':
self.surface.fill(BGCOLOR, block_location)
self.surface.blit(with_tetromino[(y, x)][1],
block_location)
def gameover(self, full_exit=False):
"""
Gameover occurs when a new tetromino does not fit after the old one has died, either
after a "natural" drop or a hard drop by the player. That is why `self.lock_tetromino`
is responsible for checking if it's game over.
"""
write_score(self.score)
if full_exit:
if self.agent_mode == True:
print("Runs completed.")
self.serialize_agent()
exit()
else:
if self.agent_mode == True:
self.agent.complete_episode()
#Manages the starting of a new game
if self.agent.get_current_episode(
) < self.agent.get_number_of_episodes():
#Resets the board
self.matrix = dict()
for y in range(MATRIX_HEIGHT):
for x in range(MATRIX_WIDTH):
self.matrix[(y, x)] = None
self.score = 0
self.lines = 0
self.board = agent.board(
self.create_board_representation())
self.board.set_board_height()
self.board.set_holes()
self.board.set_column_differences()
self.agent.set_current_board(self.board)
print(str(self.board))
new_seed = self.agent.load_new_seed()
if new_seed == None:
try:
raise ValueError(
"Not enough seeds for current experiment!")
except:
print(
"\nNot enough seeds for current experiment!\nExiting Matris..."
)
exit()
print("Generating new game with seed: " + str(new_seed))
random.seed(new_seed)
self.set_tetrominoes()
self.next_tetromino = random.choice(list_of_tetrominoes)
self.agent.set_agent_tetromino(self.current_tetromino)
#Agent's first move of the new game
self.tetromino_placement = self.agent.make_move()
self.tetromino_position = (0, self.tetromino_placement[2])
for rotations in range(self.tetromino_placement[0]):
self.request_rotation()
else:
print("Runs completed.")
self.serialize_agent()
exit()
else:
raise GameOver("Sucker!")
def place_shadow(self):
"""
Draws shadow of tetromino so player can see where it will be placed
"""
posY, posX = self.tetromino_position
while self.blend(position=(posY, posX)):
posY += 1
position = (posY - 1, posX)
return self.blend(position=position, shadow=True)
def fits_in_matrix(self, shape, position):
"""
Checks if tetromino fits on the board
"""
posY, posX = position
for x in range(posX, posX + len(shape)):
for y in range(posY, posY + len(shape)):
if self.matrix.get((y, x), False) is False and shape[y - posY][
x - posX]: # outside matrix
return False
return position
def request_rotation(self):
"""
Checks if tetromino can rotate
Returns the tetromino's rotation position if possible
"""
rotation = (self.tetromino_rotation + 1) % 4
shape = self.rotated(rotation)
y, x = self.tetromino_position
position = (self.fits_in_matrix(shape, (y, x))
or self.fits_in_matrix(shape, (y, x + 1))
or self.fits_in_matrix(shape, (y, x - 1))
or self.fits_in_matrix(shape, (y, x + 2))
or self.fits_in_matrix(shape, (y, x - 2)))
# ^ That's how wall-kick is implemented
if position and self.blend(shape, position):
self.tetromino_rotation = rotation
self.tetromino_position = position
self.needs_redraw = True
return self.tetromino_rotation
else:
return False
def request_movement(self, direction):
"""
Checks if teteromino can move in the given direction and returns its new position if movement is possible
"""
posY, posX = self.tetromino_position
if direction == 'left' and self.blend(position=(posY, posX - 1)):
self.tetromino_position = (posY, posX - 1)
self.needs_redraw = True
return self.tetromino_position
elif direction == 'right' and self.blend(position=(posY, posX + 1)):
self.tetromino_position = (posY, posX + 1)
self.needs_redraw = True
return self.tetromino_position
elif direction == 'up' and self.blend(position=(posY - 1, posX)):
self.needs_redraw = True
self.tetromino_position = (posY - 1, posX)
return self.tetromino_position
elif direction == 'down' and self.blend(position=(posY + 1, posX)):
self.needs_redraw = True
self.tetromino_position = (posY + 1, posX)
return self.tetromino_position
else:
return False
def rotated(self, rotation=None):
"""
Rotates tetromino
"""
if rotation is None:
rotation = self.tetromino_rotation
return rotate(self.current_tetromino.shape, rotation)
def block(self, color, shadow=False):
"""
Sets visual information for tetromino
"""
colors = {
'blue': (105, 105, 255),
'yellow': (225, 242, 41),
'pink': (242, 41, 195),
'green': (22, 181, 64),
'red': (204, 22, 22),
'orange': (245, 144, 12),
'cyan': (10, 255, 226)
}
if shadow:
end = [90] # end is the alpha value
else:
end = [
] # Adding this to the end will not change the array, thus no alpha value
border = Surface((BLOCKSIZE, BLOCKSIZE), pygame.SRCALPHA, 32)
border.fill(list(map(lambda c: c * 0.5, colors[color])) + end)
borderwidth = 2
box = Surface(
(BLOCKSIZE - borderwidth * 2, BLOCKSIZE - borderwidth * 2),
pygame.SRCALPHA, 32)
boxarr = pygame.PixelArray(box)
for x in range(len(boxarr)):
for y in range(len(boxarr)):
boxarr[x][y] = tuple(
list(
map(
lambda c: min(255, int(c * random.uniform(
0.8, 1.2))), colors[color])) + end)
del boxarr # deleting boxarr or else the box surface will be 'locked' or something like that and won't blit.
border.blit(box, Rect(borderwidth, borderwidth, 0, 0))
return border
def lock_tetromino(self):
"""
This method is called whenever the falling tetromino "dies". `self.matrix` is updated,
the lines are counted and cleared, and a new tetromino is chosen.
"""
self.matrix = self.blend()
lines_cleared = self.remove_lines()
if lines_cleared == -1: #Indicates that clearing the lines failed. This is due to the tetromino reaching higher than 2 above the skyline.
"""
End episode:
game will be in a terminal state as the skyline was occupied 3 cells high
however MaTris can only handle the skyline being occupied by 2 cells high.
This causes the memory to be stored as if it were a terminal state.
The board is then cleared, and a new episode restarted.
"""
self.agent.remember_state_action(self.agent.previous_state,
self.agent.previous_action, -1000,
self.agent.get_current_board(),
True)
self.agent.update_approximater()
self.agent.reset_approximaters()
self.gameover()
else:
self.lines += lines_cleared
if lines_cleared:
self.score += 100 * (lines_cleared**2) * self.combo
if not self.played_highscorebeaten_sound and self.score > self.highscore:
self.played_highscorebeaten_sound = True
if self.lines >= self.level * 10:
self.level += 1
self.combo = self.combo + 1 if lines_cleared else 1
self.set_tetrominoes()
if not self.blend() and lines_cleared != -1:
self.gameover()
self.needs_redraw = True
if self.agent_mode == True:
#Collects information from the board.
self.board.update_board_representation(
self.create_board_representation())
self.board.set_board_height()
self.board.set_holes()
self.board.set_column_differences()
print(str(self.board))
print("Column Height Differences:" +
str(self.board.get_column_differences()))
if self.agent.holes == True:
print("Holes: " + str(self.board.get_holes()))
if self.agent.height == True:
print("Height: " + str(self.board.get_board_height()))
print(str(self.tetromino_placement))
print("\nTetromino:")
for line in range(0, len(self.agent.agent_tetromino[0])):
print(str(self.agent.agent_tetromino[0][line]))
print("Epsilon: " + str(self.agent.epsilon))
reward = self.agent.update_score_and_lines(self.score, self.lines)
print("Score: " + str(self.agent.score))
print("Lines Cleared: " + str(self.agent.lines_cleared))
print("Current Episode number: " +
str(self.agent.current_episode + 1) + " / " +
str(self.agent.number_of_episodes))
print("**********************************")
#Passes tetromino and board information to the agent.
self.agent.set_agent_tetromino(self.current_tetromino)
self.agent.set_current_board(self.board)
#Remembers previous S,A,R,S
if self.agent.check_game_over(
) and lines_cleared != -1: #Ends episode if previous turn was terminal
#End of episode
if self.agent.random_moves == False:
self.agent.remember_state_action(
self.agent.previous_state, self.agent.previous_action,
-1000, self.agent.get_current_board(), True)
self.agent.update_approximater()
self.agent.reset_approximaters()
self.gameover()
else: #Continue episode as not in terminal state
self.tetromino_placement = self.agent.make_move()
if self.tetromino_placement == False:
#Tetromino placed in state that causes a game over
if self.agent.random_moves == False:
#Tetromino placed in state that causes a game over
self.agent.remember_state_action(
self.agent.previous_state,
self.agent.previous_action, -1000,
self.agent.get_current_board(), True)
self.agent.update_approximater()
self.agent.reset_approximaters()
self.gameover()
else:
#Tetromino placed in a non-terminal state.
if self.agent.random_moves == False:
self.agent.remember_state_action(
self.agent.previous_state,
self.agent.previous_action, reward,
self.agent.get_current_board(), False)
self.agent.update_approximater()
self.agent.reset_approximaters()
self.tetromino_position = (0, self.tetromino_placement[2])
for rotations in range(self.tetromino_placement[0]):
self.request_rotation()
def remove_lines(self):
"""
Removes lines from the board
"""
try:
lines = []
for y in range(MATRIX_HEIGHT):
#Checks if row if full, for each row
line = (y, [])
for x in range(MATRIX_WIDTH):
if self.matrix[(y, x)]:
line[1].append(x)
if len(line[1]) == MATRIX_WIDTH:
lines.append(y)
for line in sorted(lines):
#Moves lines down one row
for x in range(MATRIX_WIDTH):
self.matrix[(line, x)] = None
for y in range(0, line + 1)[::-1]:
for x in range(MATRIX_WIDTH):
self.matrix[(y, x)] = self.matrix.get((y - 1, x), None)
return len(lines)
except:
print("ERROR REMOVING LINES:\t DEBUG INFORMATION")
print(self.tetromino_placement)
print(self.board.board_representation)
return -1
def blend(self, shape=None, position=None, matrix=None, shadow=False):
"""
Does `shape` at `position` fit in `matrix`? If so, return a new copy of `matrix` where all
the squares of `shape` have been placed in `matrix`. Otherwise, return False.
This method is often used simply as a test, for example to see if an action by the player is valid.
It is also used in `self.draw_surface` to paint the falling tetromino and its shadow on the screen.
"""
if shape is None:
shape = self.rotated()
if position is None:
position = self.tetromino_position
copy = dict(self.matrix if matrix is None else matrix)
posY, posX = position
for x in range(posX, posX + len(shape)):
for y in range(posY, posY + len(shape)):
if (copy.get((y, x), False) is False and
shape[y - posY][x -
posX] # shape is outside the matrix
or # coordinate is occupied by something else which isn't a shadow
copy.get((y, x)) and shape[y - posY][x - posX]
and copy[(y, x)][0] != 'shadow'):
return False # Blend failed; `shape` at `position` breaks the matrix
elif shape[y - posY][x - posX]:
copy[(y, x)] = ('shadow',
self.shadow_block) if shadow else (
'block', self.tetromino_block)
return copy
def construct_surface_of_next_tetromino(self):
"""
Draws the image of the next tetromino
"""
shape = self.next_tetromino.shape
surf = Surface((len(shape) * BLOCKSIZE, len(shape) * BLOCKSIZE),
pygame.SRCALPHA, 32)
for y in range(len(shape)):
for x in range(len(shape)):
if shape[y][x]:
surf.blit(self.block(self.next_tetromino.color),
(x * BLOCKSIZE, y * BLOCKSIZE))
return surf
def create_board_representation(self):
lines = []
for y in range(MATRIX_HEIGHT):
#Checks if row if full, for each row
line = (y, [])
for x in range(MATRIX_WIDTH):
if self.matrix[(y, x)]:
line[1].append(1)
else:
line[1].append(0)
lines.append(line[1])
board = []
for i in range(len(lines)):
board.append(lines[i])
return board
def serialize_agent(self):
"""
Serializes the agent.
This saves the epsilon value, whether holes or height was used and the current ANN of the agent.
"""
agent_information = [
self.agent.epsilon, self.agent.holes, self.agent.height,
self.agent.current_net
]
handler = open(self.agent.file_path + ".obj", 'wb')
pickle.dump(agent_information, handler)
handler.close()
def gen(self):
fh = open(self.package_name + ".sv", "w")
fh.write(self.header.replace("file_name", self.package_name + ".sv"))
fh.write("`ifndef _%s_\n" % (self.package_name.upper()))
fh.write("`define _%s_\n" % (self.package_name.upper()))
fh.write("\n")
fh.write("package %s;\n" % (self.package_name))
fh.write(" import uvm_pkg::*;\n")
fh.write("\n")
fh.write(" `include \"%s.sv\"\n" % (self.defines_name))
fh.write(" `include \"%s.sv\"\n" % (self.config_name))
fh.write(" `include \"%s.sv\"\n" % (self.transaction_name))
fh.write(" `include \"%s.sv\"\n" % (self.config_name))
fh.write(" `include \"%s.sv\"\n" % (self.callback_name))
fh.write(" `include \"%s.sv\"\n" % (self.cov_callback_name))
fh.write(" `include \"%s.sv\"\n" % (self.master_driver_name))
fh.write(" `include \"%s.sv\"\n" % (self.master_sequencer_name))
fh.write(" `include \"%s.sv\"\n" % (self.master_sequence_name))
fh.write(" `include \"%s.sv\"\n" % (self.slave_driver_name))
fh.write(" `include \"%s.sv\"\n" % (self.slave_sequencer_name))
fh.write(" `include \"%s.sv\"\n" % (self.slave_sequence_name))
fh.write(" `include \"%s.sv\"\n" % (self.monitor_name))
fh.write(" `include \"%s.sv\"\n" % (self.master_agent_name))
fh.write(" `include \"%s.sv\"\n" % (self.slave_agent_name))
fh.write("\n")
fh.write("endpackage: %s\n" % (self.package_name))
fh.write("\n")
fh.write("`endif //_%s_\n" % (self.package_name.upper()))
fh.close()
#Generate agent components
agent_defines = defines.defines(self.header, self.agent_setting)
agent_defines.gen()
agent_interface = interface.interface(self.header, self.agent_setting)
agent_interface.gen()
agent_cfg = cfg.cfg(self.header, self.agent_setting)
agent_cfg.gen()
agent_transaction = transaction.transaction(self.header,
self.agent_setting)
agent_transaction.gen()
agent_sequencer = sequencer.sequencer(self.header, self.agent_setting)
agent_sequencer.sequencer_gen()
agent_sequence = sequence.sequence(self.header, self.agent_setting)
agent_sequence.sequence_gen()
agent_drv = driver.driver(self.header, self.agent_setting)
agent_drv.master_driver_gen()
agent_drv.slave_driver_gen()
agent_mon = monitor.monitor(self.header, self.agent_setting)
agent_mon.monitor_gen()
agent_callback = callback.callback(self.header, self.agent_setting)
agent_callback.callback_gen()
agent_callback.cov_callback_gen()
agent_agent = agent.agent(self.header, self.agent_setting)
agent_agent.agent_gen()
def genesis(size):
population = [agent() for _ in range(size)]
population = genes(population)
return population
from arguments import get_args
from baselines.common.atari_wrappers import make_atari
from baselines import bench
from baselines import logger
from baselines.common.atari_wrappers import wrap_deepmind
from agent import agent
import os
if __name__ == '__main__':
if not os.path.exists('logs/'):
os.mkdir('logs/')
envArgs = get_args()
logAddress = 'logs/' + envArgs.env_name + '/'
if not os.path.exists(logAddress):
os.mkdir(logAddress)
logger.configure(logAddress)
# start to create the environment
environment = make_atari(envArgs.env_name)
environment = wrap_deepmind(environment, frame_stack=True)
environment = bench.Monitor(environment, logger.get_dir())
# train the agent
trainer = agent(environment, envArgs)
trainer.learn()
environment.close()
def init_agents():
for k in range(2, 26):
soc = random.normal(g.average_soc, g.sd_average_soc, 1)
g.agents[k] = agent(soc, k, g.time)
g.agents[k].copy_all = g.copy_al
def __init__(self,
name=None,
discount=0.9,
lr=None,
alpha=1,
policy_type='greedy',
policy_param={
'eps': 0.05,
'min_eps': 0.01,
'eps_decay': 0.9999
},
env=hb,
suits='rgbyp',
players=3,
mode='standard',
hidden_layers=[200, 200, 200, 150, 100],
batch_size=512,
l1=0,
optimizer='adagrad',
mem_size=2000,
max_steps=130,
plot_frequency=1,
discrete_agents=True,
Double_DQN_version=1,
accelerated=True,
games_per_epoch=100):
self.name = name
self.weights_dir = model_directory
# if self.name == None:
# date = str(time.strftime('%m%d-%H%M'))
# self.name = f'{date}-{mode}-{suits}'
if self.name != None:
self.model_file = os.path.join(self.weights_dir, self.name + '.h5')
self.env = hb.hanabi_env(players, suits, mode)
self.iterations_done = 0
self.gamma = discount
self.learning_rate = lr
self.alpha = alpha
self.max_steps = max_steps
self.policy_param = policy_param
self.hidden_layers = hidden_layers
self.discrete_agents = discrete_agents
self.epoch = 0
self.epoch_size = games_per_epoch
self.epoch_history = {}
self.epoch_history['steps'] = []
self.epoch_history['rewards'] = []
self.epoch_history['discounted_rewards'] = []
self.epoch_history['rps'] = []
self.epoch_history['loss'] = []
self.batch_size = batch_size
self.plot_frequency = plot_frequency
self.suits = suits
self.mem_size = mem_size
self.players = players
self.mode = mode
self.l1 = l1
self.Double_DQN_version = Double_DQN_version
self.optimizer = get_optimizer(optimizer, lr)
self.action_map = self._create_action_map()
self.action_space = len(self.action_map)
self.action_totals = [0] * self.action_space
self.accelerated = accelerated
move_func = self._create_valid_moves_function()
self.policy = BehaviorPolicy(self.action_space,
move_func,
policy_type=policy_type,
param=policy_param)
if self.name != None and os.path.exists(self.model_file):
self.online_model = models.load_model(self.model_file)
self.target_model = models.load_model(self.model_file)
self.target_model.name = 'target_' + self.target_model.name
else:
self.online_model = create_Q_model(self.env, self.action_space,
self.optimizer,
self.hidden_layers,
self.learning_rate, self.l1,
'online_model')
self.online_model.name = 'online_model'
self.target_model = create_Q_model(self.env, self.action_space,
self.optimizer,
self.hidden_layers,
self.learning_rate, self.l1,
'target_model')
self.target_model.name = 'target_model'
self._freeze_target_model()
if self.accelerated:
self.training_model = training_strategy.build_accelerated_model(
self.Double_DQN_version, self.env.get_input_dim(),
self.online_model, self.target_model,
self.batch_size * self.players, self.optimizer,
self.learning_rate, self.gamma)
self._update_online_model = training_strategy.get_accelerated_update_strategy(
self.action_space,
training_model=self.training_model,
)
else:
self._update_online_model = training_strategy.get_CPU_update_strategy(
alpha, self.gamma, Double_DQN_version, self.online_model,
self.target_model)
self.player = []
for playerID in range(self.players):
self.player.append(
agent(self.env, self.online_model, self.policy.choose_action,
self.mem_size, self.action_map, playerID))
def fuzz(self, this_node=None, path=[]):
'''
Call this routine to get the ball rolling. No arguments are necessary as they are
both utilized internally during the recursive traversal of the session graph.
@type this_node: request (node)
@param this_node: (Optional, def=None) Current node that is being fuzzed.
@type path: List
@param path: (Optional, def=[]) Nodes along the path to the current one.
'''
# if no node is specified, we start from root and initialize the session.
if not this_node:
# we can't fuzz if we don't have at least one target and one request.
if not self.transport_media.media_target():
self.database.log("error",
"no target specified for job %s" %\
self.session_id)
return
if not self.edges_from(self.root.id):
self.database.log("error",
"no request specified for job %s" %\
self.session_id)
return
this_node = self.root
self.total_mutant_index = 0
self.total_num_mutations = self.num_mutations()
# If no errors above and not already connected to the agent, initialize the
# agent connection.
# If the agent cannot be initialized make sure the user is aware of it.
if self.agent == None and self.agent_settings != None:
try:
self.agent = agent(self.root_dir, self.config, self.session_id,
self.agent_settings)
self.agent.connect()
except Exception, ex:
self.database.log("error",
"failed to establish agent connection for job %s" %\
self.session_id,
str(ex))
self.finished_flag = True
self.stop_flag = True
self.save_status()
return
# Get the agent to execute
try:
self.agent.start()
except Exception, ex:
self.database.log("error",
"agent failed to execute command for job %s" %\
self.session_id,
str(ex))
self.finished_flag = True
self.stop_flag = True
self.save_status()
return
#
# MAC0425/5730 - Inteligencia Artificial - EP1 @ 2013.2
# Autor: Bruno Nunes Leal Faria - nUSP: 8765551
#
# FILE: environment.py
#
import agent
import search
import time
a = agent.agent()
# enviroment class
# @map - multi dimensional array to hold mine map
# @agent - search type to execute
class environment:
def __init__(self):
self.map = [[]]
self.graph = {}
self.size = 0
self.gold_count = 0
self.gold_left = []
self.gold_locations = []
self.search_type = None
# creates matrix from file
def create_matrix(self, stream):
x = 0
y = 0
# read dimension
c = stream.readline()
def __init__(self, inputAgentSigmaNoise=0.1):
pygame.init()
# RGB color
self.__white = (255, 255, 255)
self.__black = (0, 0, 0)
self.__red = (255, 0, 0)
self.__green = (0, 155, 0)
self.__blue = (0, 0, 255)
# give the game a title
pygame.display.set_caption("Keepaway")
self.keeperScore = 0
# these are more or less global variables..
# I'm not sure if this is bad or not.
self.__worldImage = pygame.image.load("images/soccer_field.png")
self.__ballImage = pygame.image.load("images/ball.png")
self.__keeperImage = pygame.image.load("images/keeper.png")
self.__takerImage = pygame.image.load("images/taker.png")
self.__predictedImage = pygame.image.load("images/x.png")
self.__debugYellowDotImage = pygame.image.load("images/yellow_dot.png")
self.__debugRedDotImage = pygame.image.load("images/red_dot.png")
# block sizes are used for collision detection
# only 1 size per element because all blocks are squares. block size = side length
self.__agent_block_size = 23
self.ball_block_size = 12
self.maxBallSpeed = 4
self.maxPlayerSpeed = 2
# dimensions of the game are the same as the soccer field image
self.__display_width = 550
self.display_height = 357
self.__field_center = (self.__display_width / 2, self.display_height / 2)
# gameDisplay is a pygame.surface object. it's your screen
self.gameDisplay = pygame.display.set_mode((self.__display_width, self.display_height))
self.test_fps = 60
self.train_fps = 10000
self.clock = pygame.time.Clock()
# start the ball kinda close to the keeper in the upper left corner
self.fieldBall = ball.ball((self.__field_center[0] / 4, self.__field_center[1] / 4), self.maxBallSpeed)
# setup all the initial keepers and takers. They are all starting at different field positions, which is why
# you can't have a for loop just iterate and declare all of them
types = ["keeper", "taker"]
self.agentSigmaError = inputAgentSigmaNoise
self.keeperArray = []
self.keeperTruePosArray = []
self.keeperTruePosArray.append((12.5, 12.5))
self.keeperTruePosArray.append((25, self.__display_width - 37.5))
self.keeperTruePosArray.append((self.display_height - 37.5, self.__display_width - 37.5))
self.keeperArray.append(
agent.agent(
self,
0,
kUtil.getNoisyVals(self.keeperTruePosArray[0], self.agentSigmaError),
self.agentSigmaError,
types[0],
kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError),
self.maxPlayerSpeed,
self.maxBallSpeed,
)
)
self.keeperArray.append(
agent.agent(
self,
1,
kUtil.getNoisyVals(self.keeperTruePosArray[1], self.agentSigmaError),
self.agentSigmaError,
types[0],
kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError),
self.maxPlayerSpeed,
self.maxBallSpeed,
)
)
self.keeperArray.append(
agent.agent(
self,
2,
kUtil.getNoisyVals(self.keeperTruePosArray[2], self.agentSigmaError),
self.agentSigmaError,
types[0],
kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError),
self.maxPlayerSpeed,
self.maxBallSpeed,
)
)
self.takerArray = []
self.takerTruePosArray = []
self.takerTruePosArray.append((self.display_height - 25, 25))
self.takerTruePosArray.append((self.display_height - 37.5, 50))
self.takerArray.append(
agent.agent(
self,
0,
self.takerTruePosArray[0],
self.agentSigmaError,
types[1],
kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError),
self.maxPlayerSpeed,
self.maxBallSpeed,
)
)
self.takerArray.append(
agent.agent(
self,
1,
self.takerTruePosArray[1],
self.agentSigmaError,
types[1],
kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError),
self.maxPlayerSpeed,
self.maxBallSpeed,
)
)
# 3 different font sizes
self.smallfont = pygame.font.SysFont("comicsansms", 25) # 25 is font sizes
self.medfont = pygame.font.SysFont("comicsansms", 50)
self.largefont = pygame.font.SysFont("comicsansms", 80)
self.verysmallfont = pygame.font.SysFont("comicsansms", 12)
from grid import grid
from gridQController import gridQController
from agent import agent
import matplotlib.pyplot as plt
gridSize = 5
grid = grid(gridSize)
controller = gridQController(gridSize)
agentSmith = agent(grid, controller)
iterations = 200000
rewards = [0] * iterations
for i in range(0, iterations):
agentSmith.step()
if i > 100000:
controller.setGreed(1)
rewards[i] = agentSmith.getReward()
plt.plot(rewards)
plt.show()
print("Total reward: " + str(agentSmith.getReward()) + " Iterations: " +
str(iterations) + " Success rate: " +
str(agentSmith.getReward() / iterations))
def __init__(self, phone_number, pin):
self.agent = agent()
self.agent.autho(phone_number, pin)
self.steps = []
def __init__(self, inputAgentSigmaNoise = .1, alreadyTrained = True, bevCustomTileSize = None):
pygame.init()
#RGB color
self.__white = (255,255,255)
self.__black = (0,0,0)
self.__red = (255,0,0)
self.__green = (0,155,0)
self.__blue = (0,0,255)
#give the game a title
pygame.display.set_caption('Keepaway')
self.keeperScore = 0
#these are more or less global variables..
#I'm not sure if this is bad or not.
self.__worldImage = pygame.image.load('images/soccer_field.png')
self.__ballImage = pygame.image.load('images/ball.png')
self.__keeperImage = pygame.image.load('images/keeper.png')
self.__keeperGoldImage = pygame.image.load('images/keeperGold.png')
self.__takerImage = pygame.image.load('images/taker.png')
self.__predictedImage = pygame.image.load('images/x.png')
self.__debugYellowDotImage = pygame.image.load('images/yellow_dot.png')
self.__debugRedDotImage = pygame.image.load('images/red_dot.png')
self.__debugBlackDotImage = pygame.image.load('images/black_dot.png')
self.__debugWhiteDotImage = pygame.image.load('images/white_dot.png')
self.__debugBlueDotImage = pygame.image.load('images/blue_dot.png')
self.__debugTakerPathTile = pygame.image.load('images/takerPathSquare.png')
self.__debugKeeperPathTile = pygame.image.load('images/keeperPathSquare.png')
self.__debugKeeperTile = pygame.image.load('images/keeperSquare.png')
self.__debugTakerTile = pygame.image.load('images/takerSquare.png')
self.__debugEmptyTile = pygame.image.load('images/emptySquare.png')
self.__debugTakerPathTileTwo = pygame.image.load('images/takerPathSquare2.png')
self.__debugKeeperPathTileTwo = pygame.image.load('images/keeperPathSquare2.png')
#block sizes are used for collision detection
#only 1 size per element because all blocks are squares. block size = side length
self.__agent_block_size = 23
self.__ball_block_size = 12
self.maxBallSpeed= 4
self.maxPlayerSpeed = 2
#self.rDecision = None
#dimensions of the game are the same as the soccer field image
self.__display_width = 550
self.__display_height = 357
self.displayGraphics = True
self.__field_center = (self.__display_width / 2 , self.__display_height / 2)
#gameDisplay is a pygame.surface object. it's your screen
self.gameDisplay = pygame.display.set_mode((self.__display_width,self.__display_height))
self.test_fps = 60
self.train_fps = 10000
self.clock = pygame.time.Clock()
#start the ball kinda close to the keeper in the upper left corner
self.fieldBall = ball.ball( (self.__field_center[0]/4, self.__field_center[1]/4), self.maxBallSpeed)
#the simple state variables for agents like NEAT, novelty search, and maybe sarsa
self.simpleStateVars = None
self.alreadyTrained = alreadyTrained #False if you want agent to learn and True if you want to demo
#setup all the initial keepers and takers. They are all starting at different field positions, which is why
#you can't have a for loop just iterate and declare all of them
types = ["keeper", "taker"]
self.agentSigmaError = inputAgentSigmaNoise
self.keeperArray = []
self.keeperTruePosArray = []
self.keeperTruePosArray.append((12.5, 12.5))
self.keeperTruePosArray.append((25, self.__display_width - 37.5))
self.keeperTruePosArray.append((self.__display_height - 37.5, self.__display_width - 37.5))
self.keeperArray.append(agent.agent(self, 0, kUtil.getNoisyVals( self.keeperTruePosArray[0], self.agentSigmaError), self.agentSigmaError, types[0], kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError), self.maxPlayerSpeed, self.maxBallSpeed))
self.keeperArray.append(agent.agent(self, 1, kUtil.getNoisyVals( self.keeperTruePosArray[1], self.agentSigmaError), self.agentSigmaError, types[0], kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError), self.maxPlayerSpeed, self.maxBallSpeed))
self.keeperArray.append(agent.agent(self, 2, kUtil.getNoisyVals( self.keeperTruePosArray[2], self.agentSigmaError), self.agentSigmaError, types[0], kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError), self.maxPlayerSpeed, self.maxBallSpeed))
self.takerArray = []
self.takerTruePosArray = []
self.takerTruePosArray.append((self.__display_height - 25, 25))
self.takerTruePosArray.append((self.__display_height - 37.5, 50))
self.takerArray.append(agent.agent(self, 0, self.takerTruePosArray[0], self.agentSigmaError, types[1], kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError), self.maxPlayerSpeed, self.maxBallSpeed))
self.takerArray.append(agent.agent(self, 1, self.takerTruePosArray[1], self.agentSigmaError, types[1], kUtil.getNoisyVals(self.fieldBall.trueBallPos, self.agentSigmaError), self.maxPlayerSpeed, self.maxBallSpeed))
#3 different font sizes
self.smallfont = pygame.font.SysFont("comicsansms",25) #25 is font sizes
self.medfont = pygame.font.SysFont("comicsansms",50)
self.largefont = pygame.font.SysFont("comicsansms",80)
self.verysmallfont = pygame.font.SysFont("comicsansms", 12)
#birdsEyeView generator for agents like hyperNEAT:
if bevCustomTileSize == None:
bevCustomTileSize = self.__agent_block_size
self.bev = birdsEyeView.birdsEyeView(self.__display_width, self.__display_height, bevCustomTileSize, self.__ball_block_size )
self.bev_grid_as_grid = self.bev.getBirdsEyeView(self.keeperArray, self.takerArray);
self.bev_grid_as_list = self.bev.getBirdsEyeViewAsList(self.keeperArray, self.takerArray);
self.bev_substrate = self.bev.getSubstrate(self.keeperArray, self.takerArray);
self.bev_keeper_sub_index = self.bev.getBallHolderTile(self.keeperArray)
sigmaM = 10.
sigmaN = 1.
# time length for ticks per time period
deltaT = 100
# holder list for agent objects
agentList = []
# price, return, and volume time series
price = pf * np.ones(Tmax + 1)
ret = np.zeros(Tmax + 1)
totalV = np.zeros(Tmax + 1)
rprice = np.zeros((Tmax + 1) / 100)
# create agents in list of objects
for i in range(nAgents):
agentList.append(agent(sigmaF, sigmaM, sigmaN, kMax, Lmin, Lmax))
# create set of
forecastSet = forecasts(Lmax, pf, sigmae)
# create order book
marketBook = orderBook(600., 1400., deltaP)
# set up initial prices
price[0:Tinit] = pf * (1. + 0.001 * np.random.randn(Tinit))
ret[0:Tinit] = 0.001 * np.random.randn(Tinit)
for t in range(Tinit, Tmax):
# update all forecasts
forecastSet.updateForecasts(t, price[t], ret)
tradePrice = -1
# draw random agent
randomAgent = agentList[np.random.randint(1, nAgents)]
def agent_ip():
agentout = open('agent_ip.txt','w')
ag = agent()
ag.get_ip(agentout)
agentout.close()
parser.add_argument("--exploration", type=float, default=0.2)
parser.add_argument("--save_freq", type=int, default=100)
parser.add_argument("--save_folder", type=str, default="model")
parser.add_argument("--reload", type=str, default=None)
args = parser.parse_args()
gmm = gameMgr.tetris(20, 10)
epoch = 0
write_epoch = 100
reward_history = collections.deque(maxlen=1000)
loss_history = collections.deque(maxlen=1000)
agt = agent.agent(gmm.getActionList(),
gmm.getStateSize(),
n_batch=args.batch_size,
replay_size=args.replay_size,
learning_rate=args.learn_rate,
discountRate=args.discount_rate,
saveFreq=args.save_freq,
saveFolder=args.save_folder,
memoryLimit=args.memory_limit)
if args.reload: agt.load(args.reload)
fig = plt.figure(figsize=(gmm.getScreenSize()[0], gmm.getScreenSize()[1]))
fig.canvas.set_window_title("TeTris")
setFile = file(os.path.join(args.save_folder, "settings.dat"), "w")
setFile.write(str(args))
setFile.close()
logFile = file(os.path.join(args.save_folder, "log.dat"), "w")
logCSV = csv.writer(logFile)
logCSV.writerow([
"epoch", "last_loss", "loss_mean", "last_reward", "mean_reward",
def parameter_camp_test(parameter_list):
"""
This function should take a camp ID, train an agent for that specific campaign
and then test the agent for that campaign. We start by defining the hyper-parameters.
It (currently) takes the whole campaign as an episode.
"""
epsilon_max = 0.9
epsilon_min = 0.05
discount_factor = 1
batch_size = 32
memory_cap = 100000
update_frequency = 100
episode_length = 96
camp_id = parameter_list[0]
budget_scaling = parameter_list[1]
initial_Lambda = parameter_list[2]
epsilon_decay_rate = parameter_list[3]
budget_init_var = parameter_list[4] * budget_scaling
step_length = parameter_list[5]
learning_rate = parameter_list[6]
seed = parameter_list[7]
action_size = 7
state_size = 5
tf.reset_default_graph()
np.random.seed(seed)
tf.set_random_seed(seed)
sess = tf.Session()
rtb_agent = agent(epsilon_max, epsilon_min, epsilon_decay_rate,
discount_factor, batch_size, memory_cap, state_size,
action_size, learning_rate, sess)
camp_n = [
'1458', '2259', '2997', '2821', '3358', '2261', '3386', '3427', '3476'
]
train_file_dict, test_file_dict = get_data(camp_n)
test_file_dict = test_file_dict[camp_id]
total_budget = 0
total_impressions = 0
global_step_counter = 0
for i in camp_n:
rtb_environment = RTB_environment(train_file_dict[i], episode_length,
step_length)
total_budget += train_file_dict[i]['budget']
total_impressions += train_file_dict[i]['imp']
while rtb_environment.data_count > 0:
episode_size = min(episode_length * step_length,
rtb_environment.data_count)
budget = train_file_dict[i]['budget'] * min(rtb_environment.data_count, episode_size) \
/ train_file_dict[i]['imp'] * budget_scaling
budget = np.random.normal(budget, budget_init_var)
state, reward, termination = rtb_environment.reset(
budget, initial_Lambda)
while not termination:
action, _, _ = rtb_agent.action(state)
next_state, reward, termination = rtb_environment.step(action)
memory_sample = (action, state, reward, next_state,
termination)
rtb_agent.replay_memory.store_sample(memory_sample)
rtb_agent.q_learning()
if global_step_counter % update_frequency == 0:
rtb_agent.target_network_update()
rtb_agent.e_greedy_policy.epsilon_update(global_step_counter)
state = next_state
global_step_counter += 1
epsilon = rtb_agent.e_greedy_policy.epsilon
budget = total_budget / total_impressions * test_file_dict[
'imp'] * budget_scaling
imp, click, cost, wr, ecpc, ecpi, camp_info = drlb_test(
test_file_dict, budget, initial_Lambda, rtb_agent, episode_length,
step_length)
sess.close()
lin_bid_result = list(
lin_bidding_test(train_file_dict[camp_id], test_file_dict, budget,
'historical'))
rand_bid_result = list(
rand_bidding_test(train_file_dict[camp_id], test_file_dict, budget,
'uniform'))
result_dict = {
'camp_id': camp_id,
'parameters': parameter_list[1:],
'epsilon': epsilon,
'total budget': budget,
'auctions': test_file_dict['imp'],
'camp_result': np.array([imp, click, cost, wr, ecpc, ecpi]).tolist(),
'budget': camp_info[0],
'lambda': camp_info[1],
'unimod': camp_info[2],
'action values': camp_info[3],
'lin_bid_result': lin_bid_result,
'rand_bid_result': rand_bid_result
}
return result_dict
SAVE_FREQ = 50 # episodes between saving the network
SAVE_PATH = "ai.data"
MIN_NO_OPS = 4 # lower limit of the no-ops inserted at the start of each episode
MAX_NO_OPS = 30 # upper limit of the no-ops inserted at the start of each episode
"""
The main training loop.
A random number of no-ops is inserted at the start of each episode.
The DQN is saved periodically.
"""
def training_loop(env, ai, total_frames):
while(env.frame_count < total_frames):
t0 = time.time()
no_ops = random.randrange(MIN_NO_OPS, MAX_NO_OPS + 1)
reward = env.run_episode(ai, no_ops)
print(env.episode, env.frame_count, reward, time.time()-t0)
if (env.episode % SAVE_FREQ) == SAVE_FREQ - 1:
ai.save(SAVE_PATH)
# Running the training
env = enviroment(GAME)
ai = agent(env.n_actions)
training_loop(env, ai, TOTAL_FRAMES)
if gpus:
try:
# Pour avoir 2 tensorflow en meme temps ( tensorboard & model )
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
#Liste GPU
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
print(e)
# Creation de l'agent et de l'environnement
agent = agent(MODEL_NAME, RESTART)
env = environement(IP, PORT, S_PER_EPISODE)
env.startClient()
# On lance le thread de training
trainer_thread = Thread(target=agent.train_in_loop, daemon=True)
trainer_thread.start()
#On attend que le thread soit OK
while not agent.training_initialized:
time.sleep(0.01)
#On genere une prediction, car la 1ere est toujours longue
print(agent.get_qs(np.ones((1, 16))))
def get_agent(features, players, args):
agent = ag.agent(features, players)
if args.load:
agent.model = load_model(args.load)
return agent
def run(rule, attacker, epochs):
torch.manual_seed(1)
start_time = time.time()
N = 10
N1 = 5
tr_split_len1 = 2000
te_split_len1 = 400
tr_split_len2 = 2000
te_split_len2 = 400
A = []
train_data1, test_data1 = readData_mnist()
train_data2, test_data2 = readData_synthetic_digits()
remaining_tr, remaining_te = train_data2, test_data2
Parameters = []
# attacker_num = 2
# attacker = [2, 7]
attacker_num = len(attacker)
# Accumulated_Loss = np.zeros((N, N))
Accumulated_Loss = np.ones((N, N))
average_train_loss, average_train_acc = [], []
average_test_loss, average_test_acc = [], []
individual_average_train_loss, individual_average_train_acc = np.zeros(
(epochs, N)), np.zeros((epochs, N))
individual_average_test_loss, individual_average_test_acc = np.zeros(
(epochs, N)), np.zeros((epochs, N))
for k in range(0, N):
net = Net().to(device)
# print(net)
# summary(net, (1,28,28), batch_size=-1)
a = agent(net)
A.append(a)
Parameters.append({})
for name, param in a.net.named_parameters():
if param.requires_grad:
Parameters[k][name] = param.data
for epoch in range(epochs):
print('epoch {}'.format(epoch + 1))
Train_loader_iter = []
Test_loader = []
total_train_loss = 0.
total_train_acc = 0.
total_eval_loss = 0.
total_eval_acc = 0.
remaining_tr, remaining_te = train_data2, test_data2
Count = np.zeros((N, ))
ave_train_loss = 0.
ave_train_acc = 0.
ave_eval_loss = 0.
ave_eval_acc = 0.
nanCount = 0
for k in range(0, N):
a = A[k]
a.train_loss = 0.
a.train_acc = 0.
if k < N1:
train_loader_no, test_loader_no = generateData_mnist(
train_data1, test_data1, tr_split_len1, te_split_len1, k)
else:
train_loader_no, test_loader_no, remaining_tr, remaining_te = generateData_synthetic_digits(
remaining_tr, remaining_te, tr_split_len2, te_split_len2)
Train_loader_iter.append(iter(train_loader_no))
Test_loader.append(test_loader_no)
# for iteration in range(0, tr_split_len//64):
# for k in range(0, N):
# training-----------------------------
try:
while True:
A_last = deepcopy(A)
Batch_X, Batch_Y = {}, {}
for k in range(0, N):
batch_x, batch_y = next(Train_loader_iter[k])
Batch_X[k] = batch_x.to(device)
Batch_Y[k] = batch_y.to(device)
if k in attacker:
continue
# 5 agents, get access to 1, 1/2, 1/3, 1/5, 1/10 data, so their models have different accuracy
if k % 5 == 0:
if random.randint(0, 1) in [0]:
continue
if k % 5 == 1:
if random.randint(0, 2) in [0, 1]:
continue
if k % 5 in [2, 3]:
if random.randint(0, 3) in [0, 1, 2]:
continue
# if k % 5 == 3:
# if random.randint(0, 9) in [0,1,2,3,4,5,6,7,8]:
# continue
a = A[k]
loss, acc = a.optimize(batch_x.to(device),
batch_y.to(device))
total_train_loss += loss
total_train_acc += acc
Count[k] += len(batch_x)
A, Accumulated_Loss = cooperation(A, A_last, Batch_X, Batch_Y,
Accumulated_Loss, rule,
attacker)
# print(Accumulated_Loss)
except StopIteration:
# print(iteration)
Eval_count = np.zeros((N, ))
for k in range(0, N):
if k in attacker:
continue
print('Agent: {:d}, Train Loss: {:.6f}, Acc: {:.6f}'.format(
k, A[k].train_loss / Count[k], A[k].train_acc / Count[k]))
individual_average_train_loss[epoch,
k] = A[k].train_loss / Count[k]
individual_average_train_acc[epoch,
k] = A[k].train_acc / Count[k]
if not (math.isnan(A[k].train_loss / Count[k])
or math.isnan(A[k].train_acc / Count[k])):
ave_train_loss += A[k].train_loss / Count[k]
ave_train_acc += A[k].train_acc / Count[k]
else:
nanCount += 1
# evaluation--------------------------------
A[k].net.eval()
eval_loss = 0.
eval_acc = 0.
for batch_x, batch_y in Test_loader[k]:
batch_x, batch_y = Variable(
batch_x, volatile=True).to(device), Variable(
batch_y, volatile=True).to(device)
out = A[k].net(batch_x)
loss_func = torch.nn.CrossEntropyLoss()
loss = loss_func(out, batch_y)
eval_loss += loss.item()
total_eval_loss += loss.item()
pred = torch.max(out, 1)[1]
num_correct = (pred == batch_y).sum()
eval_acc += num_correct.item()
total_eval_acc += num_correct.item()
Eval_count[k] += len(batch_x)
if not (math.isnan(eval_loss / Eval_count[k])
or math.isnan(eval_acc / Eval_count[k])):
ave_eval_loss += eval_loss / Eval_count[k]
ave_eval_acc += eval_acc / Eval_count[k]
print('Agent: {:d}, Test Loss: {:.6f}, Acc: {:.6f}'.format(
k, eval_loss / Eval_count[k], eval_acc / Eval_count[k]))
individual_average_test_loss[epoch,
k] = eval_loss / Eval_count[k]
individual_average_test_acc[epoch,
k] = eval_acc / Eval_count[k]
# print('Total Average Train Loss: {:.6f}, Train Acc: {:.6f}'.format(total_train_loss / sum(Count),
# total_train_acc / sum(Count)))
# average_train_loss.append(total_train_loss / sum(Count))
# average_train_acc.append(total_train_acc / sum(Count))
# print('Total Average Test Loss: {:.6f}, Test Acc: {:.6f}'.format(total_eval_loss / sum(Eval_count),
# total_eval_acc / sum(Eval_count)))
#
# print('Training time by far: {:.2f}s'.format(time.time() - start_time))
# average_test_loss.append(total_eval_loss / sum(Eval_count))
# average_test_acc.append(total_eval_acc / sum(Eval_count))
print('Total Average Train Loss: {:.6f}, Train Acc: {:.6f}'.format(
ave_train_loss / (N - nanCount - attacker_num),
ave_train_acc / (N - nanCount - attacker_num)))
average_train_loss.append(ave_train_loss /
(N - nanCount - attacker_num))
average_train_acc.append(ave_train_acc / (N - nanCount - attacker_num))
print('Total Average Test Loss: {:.6f}, Test Acc: {:.6f}'.format(
ave_eval_loss / (N - attacker_num),
ave_eval_acc / (N - attacker_num)))
print('Training time by far: {:.2f}s'.format(time.time() - start_time))
average_test_loss.append(ave_eval_loss / (N - attacker_num))
average_test_acc.append(ave_eval_acc / (N - attacker_num))
if epoch % 10 == 0 or epoch == epochs - 1:
if attacker_num == 0:
try:
os.makedirs("results")
except OSError:
print("Creation of the directory %s failed")
np.save('results/average_train_loss_%s.npy' % rule,
average_train_loss)
np.save('results/average_train_acc_%s.npy' % rule,
average_train_acc)
np.save('results/average_test_loss_%s.npy' % rule,
average_test_loss)
np.save('results/average_test_acc_%s.npy' % rule,
average_test_acc)
np.save('results/individual_average_train_loss_%s.npy' % rule,
individual_average_train_loss)
np.save('results/individual_average_train_acc_%s.npy' % rule,
individual_average_train_acc)
np.save('results/individual_average_test_loss_%s.npy' % rule,
individual_average_test_loss)
np.save('results/individual_average_test_acc_%s.npy' % rule,
individual_average_test_acc)
else:
try:
os.makedirs("results/attacked/%d" % attacker_num)
except OSError:
print("Creation of the directory %s failed")
np.save(
'results/attacked/%d/average_train_loss_%s.npy' %
(attacker_num, rule), average_train_loss)
np.save(
'results/attacked/%d/average_train_acc_%s.npy' %
(attacker_num, rule), average_train_acc)
np.save(
'results/attacked/%d/average_test_loss_%s.npy' %
(attacker_num, rule), average_test_loss)
np.save(
'results/attacked/%d/average_test_acc_%s.npy' %
(attacker_num, rule), average_test_acc)
np.save(
'results/attacked/%d/individual_average_train_loss_%s.npy'
% (attacker_num, rule), individual_average_train_loss)
np.save(
'results/attacked/%d/individual_average_train_acc_%s.npy' %
(attacker_num, rule), individual_average_train_acc)
np.save(
'results/attacked/%d/individual_average_test_loss_%s.npy' %
(attacker_num, rule), individual_average_test_loss)
np.save(
'results/attacked/%d/individual_average_test_acc_%s.npy' %
(attacker_num, rule), individual_average_test_acc)
import pygame
from Grid import Grid
from consts import SCREEN_SIZE
import agent
pygame.init()
pygame.display.set_caption('2048')
pygame.font.init()
clock = pygame.time.Clock()
clock.tick(60)
screen = pygame.display.set_mode([SCREEN_SIZE, SCREEN_SIZE])
screen.fill((127, 127, 127))
g = Grid(pygame, screen)
a = agent.agent()
running = True
agent_playing = False
g.render()
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_i:
agent_playing = not agent_playing
g.reset(hardreset=True)
break
if event.key == pygame.K_r:
g.reset()
def test_angleAgent(self):
a1 = agent.agent((0.010631645330612073, 5.000750148780534), self.unitTestSigma, "Keeper", (0, 0))
a2 = agent.agent((-0.008793653992994898, -0.0003569779220770502), self.unitTestSigma, "Taker", (0, 0))
a3 = agent.agent((5.000443882611892, -0.017223221164217175), self.unitTestSigma, "Keeper", (0, 0))
self.assertAlmostEqual(__getCosAngle(a1, a2, a3), 0, 1)
'cin': pChans[6].reader(),
'cout': pChans[7].writer(),
'cnote': pChans[8].writer(),
'items': []})
pObjects.append({
'name': 'Agent #2',
'cin': pChans[9].reader(),
'cout': pChans[10].writer(),
'cnote': pChans[11].writer(),
'items': []})
# Initialize client/player processes
pAgents = []
pAgents.append(player.player(-pChans[0], +pChans[1], +pChans[2]))
pAgents.append(agent.agent(-pChans[3], +pChans[4], +pChans[5], 'Agent #0'))
pAgents.append(agent.agent(-pChans[6], +pChans[7], +pChans[8], 'Agent #1'))
pAgents.append(agent.agent(-pChans[9], +pChans[10], +pChans[11], 'Agent #2'))
##### Initialize world
# Room channels
rChans = Channel() * 4
# Room layout:
# 0 1
# 3 2
# Room 0
# Add all player objects
rooms = []
rooms.append(room.room(
def run(rule, attacker, epochs):
torch.manual_seed(0)
start_time = time.time()
N = 30
A = []
batch_size = 10
train_data, test_data = readData()
Parameters = []
attacker_num = len(attacker)
# Accumulated_Loss = np.zeros((N, N))
Accumulated_Loss = np.ones((N, N))
middle1_neurons = 50
Train_loader, Test_loader = [], []
Val_loader_iter = []
Val_loader = []
average_train_loss, average_train_acc = [], []
average_test_loss, average_test_acc = [], []
individual_average_train_loss, individual_average_train_acc = np.zeros(
(epochs, N)), np.zeros((epochs, N))
individual_average_test_loss, individual_average_test_acc = np.zeros(
(epochs, N)), np.zeros((epochs, N))
for k in range(0, N):
# net = Net(n_feature=561, n_hidden1=middle1_neurons, n_output=6)
net = linearRegression(561, 6)
a = agent(net)
A.append(a)
train_loader_no, val_loader_no, test_loader_no = generateData(
train_data, test_data, k + 1, batch_size)
Train_loader.append(train_loader_no)
Test_loader.append(test_loader_no)
Val_loader.append(val_loader_no)
Val_loader_iter.append(iter(val_loader_no))
for epoch in range(epochs):
print('epoch {}'.format(epoch + 1))
Train_loader_iter = []
total_train_loss = 0.
total_train_acc = 0.
total_eval_loss = 0.
total_eval_acc = 0.
Count = np.zeros((N, ))
ave_train_loss = 0.
ave_train_acc = 0.
ave_eval_loss = 0.
ave_eval_acc = 0.
nanCount = 0
for k in range(0, N):
a = A[k]
a.train_loss = 0.
a.train_acc = 0.
Train_loader_iter.append(iter(Train_loader[k]))
try:
while True:
A_last = deepcopy(A)
Batch_X, Batch_Y = {}, {}
for k in range(0, N):
# if k in attacker:
# continue
batch_x, batch_y = Train_loader_iter[k].next()
Batch_X[k] = batch_x
Batch_Y[k] = batch_y
# only process 1/10 data for 1/3 of agents
if k % 3 == 0:
if random.randint(
0, 10) in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]:
continue
# if k % 3 == 0:
# train_loader = Train_loader_iter[k].next()
# batch_x, batch_y = (train_loader[0]).narrow(0,0,1), (train_loader[1]).narrow(0,0,1)
# else:
# batch_x, batch_y = Train_loader_iter[k].next()
#
# Batch_X.append(batch_x)
# Batch_Y.append(batch_y)
# if k % 3 == 0:
# if random.randint(0, 5) == 1:
# pass
# batch_x = gaussian(batch_x, 5, 5)
# batch_y = torch.LongTensor(np.random.randint(6, size=batch_size))
# if random.randint(0, 2) == 1:
# batch_y = torch.LongTensor(np.random.randint(6, size=batch_size))
# if (k+1) % 5 == 0:
# try:
# batch_x, batch_y = Train_loader_iter[k].next()
# except:
# Train_loader_iter[k] = iter(Train_loader[k])
# batch_x, batch_y = Train_loader_iter[k].next()
# else:
# batch_x, batch_y = Train_loader_iter[k].next()
a = A[k]
loss, acc = a.optimize(batch_x, batch_y)
if math.isnan(loss) or math.isnan(acc):
continue
total_train_acc += acc
# try:
# val_x, val_y = Val_loader_iter[k].next()
# except:
# Val_loader_iter[k] = iter(Val_loader[k])
# val_x, val_y = Val_loader_iter[k].next()
# Batch_X.append(val_x)
# Batch_Y.append(val_y)
Count[k] += len(batch_x)
A, Accumulated_Loss = cooperation(A, A_last, Batch_X, Batch_Y,
Accumulated_Loss, rule,
attacker)
# print(Accumulated_Loss)
except StopIteration:
# print(iteration)
Eval_count = np.zeros((N, ))
for k in range(0, N):
if k in attacker:
continue
print('Agent: {:d}, Train Loss: {:.6f}, Acc: {:.6f}'.format(
k, A[k].train_loss / Count[k], A[k].train_acc / Count[k]))
individual_average_train_loss[epoch,
k] = A[k].train_loss / Count[k]
individual_average_train_acc[epoch,
k] = A[k].train_acc / Count[k]
if not (math.isnan(A[k].train_loss / Count[k])
or math.isnan(A[k].train_acc / Count[k])):
ave_train_loss += A[k].train_loss / Count[k]
ave_train_acc += A[k].train_acc / Count[k]
else:
nanCount += 1
# evaluation--------------------------------
A[k].net.eval()
eval_loss = 0.
eval_acc = 0.
for batch_x, batch_y in Test_loader[k]:
batch_x, batch_y = Variable(
batch_x, volatile=True), Variable(batch_y,
volatile=True)
out = A[k].net(batch_x)
loss_func = torch.nn.CrossEntropyLoss()
loss = loss_func(out, batch_y)
pred = torch.max(out, 1)[1]
num_correct = (pred == batch_y).sum()
if math.isnan(loss) or math.isnan(num_correct):
continue
eval_loss += loss.item()
eval_acc += num_correct.item()
total_eval_loss += loss.item()
total_eval_acc += num_correct.item()
Eval_count[k] += len(batch_x)
if not (math.isnan(eval_loss / Eval_count[k])
or math.isnan(eval_acc / Eval_count[k])):
ave_eval_loss += eval_loss / Eval_count[k]
ave_eval_acc += eval_acc / Eval_count[k]
print('Agent: {:d}, Test Loss: {:.6f}, Acc: {:.6f}'.format(
k, eval_loss / Eval_count[k], eval_acc / Eval_count[k]))
individual_average_test_loss[epoch,
k] = eval_loss / Eval_count[k]
individual_average_test_acc[epoch,
k] = eval_acc / Eval_count[k]
try:
print('Total Average Train Loss: {:.6f}, Train Acc: {:.6f}'.format(
ave_train_loss / (N - nanCount - attacker_num),
ave_train_acc / (N - nanCount - attacker_num)))
average_train_loss.append(ave_train_loss /
(N - nanCount - attacker_num))
average_train_acc.append(ave_train_acc /
(N - nanCount - attacker_num))
print('Total Average Test Loss: {:.6f}, Test Acc: {:.6f}'.format(
ave_eval_loss / (N - attacker_num),
ave_eval_acc / (N - attacker_num)))
except:
pass
print('Training time by far: {:.2f}s'.format(time.time() - start_time))
average_test_loss.append(ave_eval_loss / (N - attacker_num))
average_test_acc.append(ave_eval_acc / (N - attacker_num))
if epoch % 10 == 0 or epoch == epochs - 1:
if attacker_num == 0:
try:
os.makedirs("results")
except OSError:
print("Creation of the directory %s failed")
np.save('results/average_train_loss_%s.npy' % rule,
average_train_loss)
np.save('results/average_train_acc_%s.npy' % rule,
average_train_acc)
np.save('results/average_test_loss_%s.npy' % rule,
average_test_loss)
np.save('results/average_test_acc_%s.npy' % rule,
average_test_acc)
np.save('results/individual_average_train_loss_%s.npy' % rule,
individual_average_train_loss)
np.save('results/individual_average_train_acc_%s.npy' % rule,
individual_average_train_acc)
np.save('results/individual_average_test_loss_%s.npy' % rule,
individual_average_test_loss)
np.save('results/individual_average_test_acc_%s.npy' % rule,
individual_average_test_acc)
else:
try:
os.makedirs("results/attacked/%d" % attacker_num)
except OSError:
print("Creation of the directory %s failed")
np.save(
'results/attacked/%d/average_train_loss_%s.npy' %
(attacker_num, rule), average_train_loss)
np.save(
'results/attacked/%d/average_train_acc_%s.npy' %
(attacker_num, rule), average_train_acc)
np.save(
'results/attacked/%d/average_test_loss_%s.npy' %
(attacker_num, rule), average_test_loss)
np.save(
'results/attacked/%d/average_test_acc_%s.npy' %
(attacker_num, rule), average_test_acc)
np.save(
'results/attacked/%d/individual_average_train_loss_%s.npy'
% (attacker_num, rule), individual_average_train_loss)
np.save(
'results/attacked/%d/individual_average_train_acc_%s.npy' %
(attacker_num, rule), individual_average_train_acc)
np.save(
'results/attacked/%d/individual_average_test_loss_%s.npy' %
(attacker_num, rule), individual_average_test_loss)
np.save(
'results/attacked/%d/individual_average_test_acc_%s.npy' %
(attacker_num, rule), individual_average_test_acc)
def simulate(self):
a = agent()
a.load_memory()
s = snake()
offset = 50
pygame.init()
screen = pygame.display.set_mode((400,450))
head_color = self.pink
snake_color = self.green
pygame.display.set_caption("snake.ai")
self.my_font = pygame.font.SysFont("arial", 24)
self.surface = self.my_font.render(self.intro, True, self.blue, self.pink)
running = True
screen.fill(self.white)
pygame.draw.rect(screen, self.green, [0, 200, 40, 40], 3)
x = 0.
direction = "right"
while running:
time.sleep(0.5)
for event in pygame.event.get():
if event.type == KEYDOWN:
if event.key == K_SPACE:
self.is_agent = not self.is_agent
self.update_word()
if event.key == K_LEFT:
direction = "left"
elif event.key == K_RIGHT:
direction = "right"
elif event.key == K_UP:
direction = "down"
elif event.key == K_DOWN:
direction = "up"
if event.type == QUIT:
print "quit"
a.output()
pygame.quit()
exit()
if not self.is_agent:
snake_color = self.green
ans = s.run(direction)
else:
snake_color = self.blue
state = a.build_state()
a.create_Q(state) # Create 'state' in Q-table
direction = a.choose_action(state)
ans = a.s.run(direction)
if ans == False:
s.reset()
direction = "right"
continue
if ans[1]:
if self.is_agent and a.learning:
self.train_time += 1
self.update_word()
if self.train_time == 10000:
a.output()
a.learning = False
screen.fill(self.white)
for index, item in enumerate(ans[0]):
if index == 0:
color = head_color
else:
# prev = ans[0][index - 1]
color = snake_color
pygame.draw.rect(screen, color, [item[0] * 40 + 2,item[1] * 40 + offset + 2, 36, 36], 0)
food = s.get_food()
pygame.draw.rect(screen, self.red, [food[0] * 40, food[1] * 40 + offset, 40, 40], 0)
screen.blit(self.surface,(0,0))
pygame.display.update()
def main(args):
env_name = args.env
env = gym.make(env_name)
a2c_agent = agent(env)
a2c_agent.train(env_name)
import sys from world import world from agent import agent import random from PyQt4 import QtGui, QtCore from map import map app = QtGui.QApplication(sys.argv) inname = sys.argv[1] mp = map(inname) wrld = world(mp) for i in range(mp.mwidth*mp.mheight/8): agent(wrld,i) wrld.show() sys.exit(app.exec_())
def qLearn():
a = agent.agent()
t = agent.target()
ql = QLearner.QLearner()
# Initialize vectors and starting coordinates for agents and targets
ql.reset_qTable()
# Create output files
learning = open('BestFit_QL.txt', 'w') # Records best fitnesses
perf = open('SystemReward_QL.txt', 'w')
rel = open('Reliability_QL.txt',
'w') # Records how successful trained NN is using "best" policy
eff = open('Alg_Time_QL.txt', 'w')
stp = open('Steps_Taken_QL.txt', 'w')
for srun in range(p.stat_runs):
print('current stat run: ', srun)
a.assign_acoords(p.x_dim, p.y_dim)
t.assign_tcoords(p.x_dim, p.y_dim, a.ax_init, a.ay_init)
time_begin = process_time()
ql.reset_qTable()
for ep in range(p.episodes):
k = 0
while k < p.steps:
ql.update_prev_state(a.agent_x, a.agent_y)
act = ql.epsilon_select()
a.agent_move(act)
ql.update_curr_state(a.agent_x, a.agent_y)
a.update_reward_QL(t.tx, t.ty)
ql.update_qTable(a.agent_reward, act)
if a.goal_captured == True:
k = p.steps # Stop iterating if target is captured
k += 1
a.reset_agent()
learning.write('%f' % np.max(ql.qtable[:, :]))
learning.write('\t') # Records max reward in Qtable
time_end = process_time()
total_time = time_end - time_begin
eff.write('%f' % total_time)
eff.write('\n')
# Test Best Policy Found
a.reset_agent()
k = 0
while k < p.steps:
ql.update_prev_state(a.agent_x, a.agent_y)
a.update_state_vec(t.tx, t.ty)
act = ql.greedy_select()
a.agent_move(act)
a.update_reward_QL(t.tx, t.ty)
if a.goal_captured == True:
stp.write('%f' % k)
stp.write('\n')
k = p.steps # Stop iterating if target is captured
k += 1
if a.goal_captured == True: # Record reliability of agent
rel.write('%d' % 1)
rel.write('\t')
else:
rel.write('%d' % 0)
rel.write('\t')
system_reward = a.agent_reward # Record system performance for stat run
perf.write('%f' % system_reward)
perf.write('\t')
learning.write('\n')
perf.write('\n')
rel.write('\n') # New line for new stat run
learning.close()
perf.close()
rel.close()
stp.close()
def run_one_episode(FM_model, user_id, busi_id, MAX_TURN, do_random, write_fp,
strategy, TopKTaxo, PN_model, gamma, trick, mini,
optimizer1_fm, optimizer2_fm, alwaysupdate, start_facet,
mask, sample_dict):
# _______ initialize user and agent _______
# Initialize the user
the_user = env.user(user_id, busi_id)
# Initialize done
numpy_list = list()
log_prob_list, reward_list = Variable(torch.Tensor()), list()
action_tracker, candidate_length_tracker = list(), list()
the_agent = agent.agent(FM_model, user_id, busi_id, do_random, write_fp,
strategy, TopKTaxo, numpy_list, PN_model,
log_prob_list, action_tracker,
candidate_length_tracker, mini, optimizer1_fm,
optimizer2_fm, alwaysupdate, sample_dict)
# _______ chat history _______
chat_history = dict()
# _______ initialize start message _______
data = dict()
# data['facet'] = choose_start_facet(busi_id)
data['facet'] = start_facet
# print('Starting facet is : {}'.format(data['facet']))
start_signal = message(cfg.AGENT, cfg.USER, cfg.EPISODE_START, data)
agent_utterance = None
while (the_agent.turn_count < MAX_TURN):
if the_agent.turn_count == 0:
user_utterance = the_user.response(start_signal)
else:
user_utterance = the_user.response(agent_utterance)
# print('The user utterance in #{} turn, type: {}, data: {}\n'.format(the_agent.turn_count, user_utterance.message_type, user_utterance.data))
with open(write_fp, 'a') as f:
f.write(
'The user utterance in #{} turn, type: {}, data: {}\n'.format(
the_agent.turn_count, user_utterance.message_type,
user_utterance.data))
if user_utterance.message_type == cfg.ACCEPT_REC:
the_agent.history_list.append(2)
print('Rec Success! in Turn: {}.'.format(the_agent.turn_count))
rewards = get_reward(the_agent.history_list, gamma, trick,
user_utterance.data)
if cfg.purpose == 'pretrain':
return numpy_list
else:
return (the_agent.log_prob_list, rewards,
the_agent.history_list)
agent_utterance = the_agent.response(user_utterance)
the_agent.turn_count += 1
if the_agent.turn_count == MAX_TURN:
the_agent.history_list.append(-2)
print('Max turn quit...')
rewards = get_reward(the_agent.history_list, gamma, trick)
if cfg.purpose == 'pretrain':
return numpy_list
else:
return (the_agent.log_prob_list, rewards,
the_agent.history_list)
from environment import environment
from agent import agent
import numpy as np
agent_o = agent('agent_o','O')
agent_x = agent('agent_x','X')
env = environment(agent_o,agent_x)
env.render(['X','O','X','O','X','O','X','O','X'])
def test__distAgent(self):
a1 = agent.agent((0, 0), self.unitTestSigma, "Keeper", (0, 0))
a2 = agent.agent((10, 10), self.unitTestSigma, "Taker", (0, 0))
self.assertAlmostEqual(__distAgent(a1, a2), math.sqrt(200), 1)
'11st_julia', category_no[current_category])
OUTPUT_FILENAME = \
'/home/taey16/storage/product/11st_julia/demo_{}.txt.wrap_size0.oversampleFalse.pickle'.format(
category_no[current_category])
if __name__ == '__main__':
print 'Start to indexing for {}'.format(INPUT_FILENAME)
print 'output will be saved in {}'.format(OUTPUT_FILENAME)
#import pdb; pdb.set_trace()
meta_filename = '{}/{}'.format(DATASET_ROOT, INPUT_FILENAME)
parser = parser_utils()
input = parser.parse(meta_filename)
agent = agent(**net_args)
agent.net.forward()
indexer = indexer(category_no, max_num_items)
item_counter = 0
for item in input:
try:
prd_no = item['__prd_no__']
fname = \
'/userdata2/index_11st_20151020/october_11st_imgdata/{}.jpg'.format(prd_no)
object_roi = item['__object_roi__'].strip().split(',')
category_id = item['__mctgr_no__']
roi = parser.get_roi_meta_dic(object_roi)
start_loading = time.time()
image = agent.load_image_roi(fname, roi, 0)
elapsed_loading = time.time() - start_loading
from environment import environment
from agent import agent
from player import player
# agent plays with human player
agent_o = agent('agent_o', 'O')
player_x = player('player_x', 'X')
env = environment(agent_o, player_x)
# X plays first
env.play(player_x)
env.close()
import agent as ag
from threading import Thread
from rgui import *
from bot import *
import time
def printTime():
while True:
print(time.ctime(time.time()))
if __name__ == '__main__':
ob=ag.agent()
bb=bot()
#bb.ru()
Thread(target=bb.ru).start()
a=GUI(ob, bb)
Thread(target=a.run).start()
#thread.start_new_thread(bb.ru)
#thread.start_new_thread(a.run)
#bb.ru()
#a.run()
canvas.create_oval((W/2.-4, H/2.-4, W/2.+4, H/2.+4), fill='goldenrod')
canvas.create_oval((W/2.-2, H/2.-2, W/2.+2, H/2.+2), fill='orange red')
for i in range(300):
x = random.random()*W
y = random.random()*H
canvas.create_oval((x-1, y-1, x+1, y+1), fill='white')
canvas.pack()
items = []
normeSpeed = 60
distToCenter = 250
for i in range(2000):
posRandom = 2*3.14*random.random()
items.append(agent(canvas,
Vec2d(W/2. + distToCenter*cos(posRandom) + 10*(2*random.random()-1),
H/2. + distToCenter*sin(posRandom) + 10*(2*random.random()-1)),
Vec2d(normeSpeed*sin(posRandom) + 10*(2*random.random()-1),
-normeSpeed*cos(posRandom) + 10*(2*random.random()-1))))
#Vec2d(normeSpeed*(2*random.random()-1), normeSpeed*(2*random.random() - 1))))
#items.append(agent(canvas,
#Vec2d(520, 300),
#Vec2d(0, 0)))
root.update() # fix geometry
# loop over items
try:
while 1:
t1 = time.time()
for agent in items:
from environment import environment
from agent import agent
agent_o = agent('agent_o', 'O', exp_rate=0.3)
agent_x = agent('agent_x', 'X', exp_rate=0.3)
env = environment(agent_o, agent_x)
#rounds = 9*8*7*6*5*4*3*2*1 * 10
rounds = 9
print(len(agent_o.Q))
env.train(agent_x, rounds)
print(len(agent_o.Q))
env.close()
plt.clf()
if agent_.env.maze.ravel()[s_] != 0:
break
if __name__ == '__main__':
train_settings()
seed()
brain_ = brain(size=args.arena_size, gamma=0.9, l_r=0.9)
env_ = env(size=args.arena_size, cat_r=[-10, -20], cheese_r=[10, 20])
agent_ = agent(env=env_, brain=brain_)
plt.imshow(env_.maze)
plt.pause(1)
for i in range(args.random_steps):
agent_.step()
if i % 10 == 0:
plt.imshow(agent_.brain.q_mat)
plt.pause(0.01)
plt.clf()
animate(agent_)
def evo_net():
nn = neuralnet.NN()
g = GA.GA()
a = agent.agent()
t = agent.target()
# Initialize vectors and starting coordinates for agents and targets
nn.create_NN(2, 3, 4) # (n_inputs, n_outputs, hidden layer size)
# Create output files
learning = open('BestFit_NN.txt', 'w') # Records best fitnesses
perf = open('SystemReward_NN.txt', 'w')
rel = open('Reliability_NN.txt',
'w') # Records how successful trained NN is using "best" policy
eff = open('Alg_Time_NN.txt', 'w')
stp = open('Steps_Taken_NN.txt', 'w')
for srun in range(p.stat_runs):
print('current stat run: ', srun)
a.assign_acoords(p.x_dim, p.y_dim)
t.assign_tcoords(p.x_dim, p.y_dim, a.ax_init, a.ay_init)
time_begin = process_time()
g.create_pop() # (policy_size)
for j in range(g.population_size): # Evaluate the initial population
nn.get_weights(g.population[j])
a.reset_agent()
k = 0
while k < p.steps: # Move around for certain number of steps unless target is captured
a.update_state_vec(t.tx, t.ty) # Updates state input to NN
nn.get_inputs(a.state_vector)
act = nn.get_ouput() # Get output from NN
a.agent_move(act) # Agent moves
a.update_reward_NN(t.tx, t.ty)
if a.goal_captured == True:
k = p.steps # Stop iterating, target is captured
k += 1
g.pop_fit[j] = a.agent_reward # Fitness is sum of agent rewards
learning.write('%f' % max(g.pop_fit))
learning.write('\t')
# Train weights or neural network
for i in range(p.generations - 1):
g.crossover()
g.mutate() # Create new population for testing
for j in range(g.population_size): # Test offspring population
nn.get_weights(g.offspring_pop[j])
a.reset_agent()
k = 0
while k < p.steps: # Move around for certain number of steps unless target is captured
a.update_state_vec(t.tx, t.ty) # Updates state input to NN
nn.get_inputs(a.state_vector)
act = nn.get_ouput() # Get output from NN
a.agent_move(act) # Agent moves
a.update_reward_NN(t.tx, t.ty)
if a.goal_captured == True:
k = p.steps # Stop iterating, target is captured
k += 1
g.pop_fit[j] = a.agent_reward
g.down_select() # Establish new parent population
learning.write('%f' % g.pop_fit[0])
learning.write('\t')
time_end = process_time()
total_time = time_end - time_begin
eff.write('%f' % total_time)
eff.write('\n')
# Test Best Policy Found
nn.get_weights(g.population[0])
a.reset_agent()
k = 0
best_fitn = max(g.pop_fit)
assert (best_fitn == g.pop_fit[0])
while k < p.steps:
a.update_state_vec(t.tx, t.ty)
nn.get_inputs(a.state_vector)
act = nn.get_ouput()
a.agent_move(act)
a.update_reward_NN(t.tx, t.ty)
if a.goal_captured == True:
stp.write('%f' % k)
stp.write('\n')
k = p.steps # Stop iterating if target is captured
k += 1
if a.goal_captured == True:
rel.write('%d' % 1)
rel.write('\t')
else:
rel.write('%d' % 0)
rel.write('\t')
system_reward = a.agent_reward
perf.write('%f' % system_reward)
perf.write('\t')
learning.write('\n')
perf.write('\n')
rel.write('\n') # New line for new stat run
learning.close()
perf.close()
rel.close()
eff.close()
stp.close()