def runCentralServer(slaveIds = [], actionset = [], *kw, **kws):
global CENTRAL_ACTION_SET, PENSIEVE_IPC_QUEUES
CENTRAL_ACTION_SET = actionset
masterQueue = mp.Queue()
slaveQueues = {x:mp.Queue() for x in slaveIds}
PENSIEVE_IPC_QUEUES = [masterQueue, slaveQueues]
p = mp.Process(target=_runCentralServer, args=(actionset, )+kw, kwargs=kws)
p.start()
return p
def __init__(self, actionset = [], infoDept=S_LEN, *arg, **kwarg):
assert PensiveLearner.__instance == None
PensiveLearner.__instance = self
self.recv, self.send = mp.Queue(), mp.Queue()
self._vActionset = actionset
self._vInfoDept = infoDept
self._vRunning = True
p = mp.Process(target=self.handle, args= (self.send, self.recv, actionset, infoDept)+arg, kwargs=kwarg)
p.start()
self.proc = p
atexit.register(self.cleanup)
def __init__(self, videoInfo, agent, log_file_path=LOG_FILE, *kw, **kws):
assert AbrPensieveClass.__instance == None
self.recv = mp.Queue()
self.send = mp.Queue()
self.proc = mp.Process(
target=self.handle,
args=(self.send, self.recv, videoInfo, agent, log_file_path) + kw,
kwargs=kws)
self.proc.start()
AbrPensieveClass.__instance = self
self.count = 0
self.agents = []
atexit.register(self.cleanup)
def __init__(self,
actionset=[],
infoDept=S_LEN,
infoDim=S_INFO,
log_path=None,
summary_dir=None,
nn_model=None,
readOnly=False,
master=False,
ipcQueues=None):
assert summary_dir
assert readOnly
assert master == (ipcQueues is not None)
self.master = master
cprint.cyan("!!CREATING OBJECT!!")
if not master:
cprint.blue("!!SLAVE!!")
self.recv, self.send = [mp.Queue(), mp.Queue()]
self.proc = mp.Process(
target=PensiveLearner,
args=(actionset, infoDept, infoDim, log_path, summary_dir,
nn_model, readOnly, True, [self.send, self.recv]))
self.proc.start()
while True:
cmd = self.recv.get()
if cmd == "ready":
break
return
cprint.red("!!MASTER!!")
self.recv, self.send = ipcQueues
# myprint("Pensieproc init Params:", actionset, infoDept, log_path, summary_dir, nn_model)
self.pid = os.getpid()
self.summary_dir = summary_dir
self.nn_model = None if not nn_model else os.path.join(
self.summary_dir, nn_model)
self.a_dim = len(actionset)
self._vActionset = actionset
self._vInfoDim = infoDim
self._vInfoDept = infoDept
self._vReadOnly = readOnly
if not os.path.exists(self.summary_dir):
os.makedirs(self.summary_dir)
self.sess = tf.Session()
self.actor = a3c.ActorNetwork(
self.sess,
state_dim=[self._vInfoDim, self._vInfoDept],
action_dim=self.a_dim,
learning_rate=ACTOR_LR_RATE)
self.critic = a3c.CriticNetwork(
self.sess,
state_dim=[self._vInfoDim, self._vInfoDept],
action_dim=self.a_dim,
learning_rate=CRITIC_LR_RATE)
self.summary_ops, self.summary_vars = a3c.build_summaries()
self.sess.run(tf.global_variables_initializer())
self.writer = tf.summary.FileWriter(
self.summary_dir, self.sess.graph) # training monitor
self.saver = tf.train.Saver() # save neural net parameters
# restore neural net parameters
self.epoch = 0
if self.nn_model is None and self.master:
nn_model, epoch = guessSavedSession(self.summary_dir)
if nn_model:
self.nn_model = nn_model
self.epoch = epoch
if self.nn_model is not None: # nn_model is the path to file
self.saver.restore(self.sess, self.nn_model)
cprint.red("Model restored with `" + self.nn_model + "'")
try:
self.runCmd()
except Exception as ex:
track = tb.format_exc()
cprint.red(track)