def train(mode):
global trainingCurrentStep
trainingCurrentStep = 0
if mode == settings.NEAT_IP_KEY:
trainMode('../config_files/config_neat_ip',
settings.NEAT_IP_EVOLVING_STEPS, eval_genomes_neat_ip)
elif mode == settings.NEAT_DYCICLE_KEY:
trainMode('../config_files/config_neat_dycicle',
settings.NEAT_DYCICLE_EVOLVING_STEPS,
eval_genomes_neat_dycicle)
elif mode == settings.NEAT_DIP_KEY:
node_names = {
-1: 'a1',
-2: 'a*1',
-3: 'a2',
-4: 'a*2',
-5: 'a0',
-6: 'a*0',
0: 'u'
}
trainMode('../config_files/config_neat_dip',
settings.NEAT_DIP_EVOLVING_STEPS, eval_genomes_neat_dip,
node_names)
elif mode == settings.NEAT_TIP_KEY:
trainMode('../config_files/config_neat_tip',
settings.NEAT_TIP_EVOLVING_STEPS, eval_genomes_neat_tip)
elif mode == settings.NEAT_WALKER_KEY:
trainMode('../config_files/config_neat_walker',
settings.NEAT_WALKER_EVOLVING_STEPS,
eval_genomes_neat_walker)
app = Application()
app.play()
def main():
parser = argparse.ArgumentParser(description='Run the program')
parser.add_argument("--trainMode", help="Mode of training")
args = parser.parse_args()
settings.TRAIN_CALLBACK = train
if args.trainMode:
train(args.trainMode)
else:
app = Application()
app.play()
