def train():
tStart = time.time()
# stack_size=4
envName = 'deadly_corridor.cfg'
game = DoomGame()
game.load_config(envName)
game.set_sound_enabled(False)
game.set_screen_resolution(ScreenResolution.RES_640X480)
game.set_window_visible(False)
game.init()
# acts = game.get_available_buttons_size()
del game
trainer = Trainer(actions=range(4), teamPopSize=30, rTeamPopSize=30)
# trainer = loadTrainer('trainer.tn')
# trainer = loadTrainer('trainer.tn')
processes = 7
man = mp.Manager()
pool = mp.Pool(processes=processes, maxtasksperchild=1)
allScores = [] # track all scores each generation
for gen in range(100): # do 100 generations of training
scoreList = man.list()
# get agents, noRef to not hold reference to trainer in each one
# don't need reference to trainer in multiprocessing
agents = trainer.getAgents()# swap out agents only at start of generation
# run the agents
pool.map(runAgent,
[(agent, envName, scoreList, 1, 2000)
for agent in agents])
# apply scores, must do this when multiprocessing
# because agents can't refer to trainer
teams = trainer.applyScores(scoreList)
# important to remember to set tasks right, unless not using task names
# task name set in runAgent()
trainer.evolve(tasks=[envName]) # go into next gen
# an easier way to track stats than the above example
scoreStats = trainer.fitnessStats
allScores.append((scoreStats['min'], scoreStats['max'], scoreStats['average']))
print('Time Taken (Hours): ' + str((time.time() - tStart)/3600))
print('Gen: ' + str(gen))
print('Results so far: ' + str(allScores))
# clear_output()
print('Time Taken (Hours): ' + str((time.time() - tStart)/3600))
print('Results:\nMin, Max, Avg')
for score in allScores:
print(score[0],score[1],score[2])
trainer.saveToFile('trainer.tn')
agents = trainer.getAgents()
agent = Agent
agent = agent.loadAgent("Magnus")
print("yo as far as I understand the load was successful?")
else:
IQ = 0
trainer = Trainer(actions=range(30),
teamPopSize=opts.popSize,
rTeamPopSize=opts.popSize,
sourceRange=310)
agents = trainer.getAgents()
agent = agents.pop()
agent.saveToFile("Magnus")
#psykerLevel = 0
trainer.saveToFile("Tzeentch")
agentScores = []
curGen = 0
#psykerLevel += agent.psykerLevel
lastState = None
# create a log file
global logName
timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
logName = "log-{}.txt".format(timestamp)
# serve until force stop
while True:
pass