#Creating the Networks and Methods of the Run.
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=settings["GPUCapacitty"],
allow_growth=True)
config = tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False,
allow_soft_placement=True)
sess = tf.Session(config=config)
with tf.device(args.processor):
Method = GetFunction(settings["Method"])
workers = Method(sess=sess,
settings=settings,
netConfigOverride=netConfigOverride)
InitializeVariables(
sess) #Included to catch if there are any uninitalized variables.
#Saving config files in the model directory
EXP_NAME = settings["RunName"]
LOG_PATH = './logs/' + EXP_NAME
CreatePath(LOG_PATH)
with open(LOG_PATH + '/runSettings.json', 'w') as outfile:
json.dump(settings, outfile)
with open(LOG_PATH + '/netConfigOverride.json', 'w') as outfile:
json.dump(netConfigOverride, outfile)
COORD = tf.train.Coordinator()
worker_threads = []
for i, worker in enumerate(workers):
if i == 0:
job = lambda: worker.work(COORD, render=args.render)
import tensorflow as tf
import numpy as np
from utils.utils import InitializeVariables, CreatePath, interval_flag, GetFunction
if __name__ == "__main__":
import numpy as np
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5, allow_growth=True)
config = tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False, allow_soft_placement=True)
sess = tf.Session(config=config)
with tf.device("/gpu:0"):
test = HierarchicalNetwork(configFile="networks/hierarchyTest.json",actionSize=4)
# s = tf.placeholder(tf.float32, [None,39,39,6], 'S')
# state={"state":s}
# out = test(state)
HPs = { "eps":0.2,
"EntropyBeta":0.00,
"CriticBeta":0.3,
"LR":0.0001,
"Gamma":0.99,
"lambda":0.9,
"Optimizer":"Adam",
"Epochs":5,
"BatchSize":1024,
"FS":2,
"MinibatchSize":32}
test2 = PPO_Hierarchy(test,sess,[10,10,3],4,HPs)
InitializeVariables(sess)
s = np.random.rand(10,10,10,3)
a,stuff=test2.GetAction(s,0)