def tf_worker(dqn: DQN, s_queue):
"""
A single process that removes evaluation tasks from a queue.
It constructs the NN in TensorFlow and uses it to evaluate board states sent to it.
Args:
dqn, an uninitialized TensorFlow object representing the DQN
s_queue, the queue of board states.
Returns:
self
"""
#Perform TensorFlow initialization
with tf.Graph().as_default() as dqn.tf_graph:
dqn.build_model()
with tf.Session() as dqn.tf_session:
dqn._init_tf()
try:
index, state = s_queue.get(True, 5)
while index != -1:
#Reshape to align with network input.
state = state.reshape(1, 263)
#Pass to Tensorflow here to evaluate
s_val = dqn.get_q_value(state, "dqn")
# (global) Update root action with evaluation; average child values
if not isclose(q_vals[index], 0.0, rel_tol=1e-6):
q_vals[index] = (q_vals[index] + s_val)/2
else:
q_vals[index] = s_val
index, state = s_queue.get(True, 5)
except Empty as e:
raise GameTreeFailure
except:
raise
def q_par(dqn, g1):
print(dqn.get_q_value(g1, "dqn"))
def par(a):
print(a)
features = 20
h1 = 10
h2 = 5
g1 = np.arange(40).reshape(2, 20)
dqn = DQN(features, h1, h2, "models/tf_multi_1")
with dqn.tf_graph.as_default():
dqn.build_model()
with tf.Session() as dqn.tf_session:
dqn._init_tf()
print(dqn.tf_session.run(dqn.model, feed_dict={dqn.s_: g1}))
processes = []
for p in range(4):
processes.append(mp.Process(target=q_par, args=(dqn, g1)))
processes[p].start()
for p in range(4):
processes[p].join()
import multiprocessing as mp
import sys
def q_par(dqn, g1):
print(dqn.get_q_value(g1, "dqn"))
def par(a):
print(a)
features = 20
h1 = 10
h2 = 5
g1 = np.arange(40).reshape(2, 20)
dqn = DQN(features, h1, h2, "models/tf_multi_1")
with dqn.tf_graph.as_default():
dqn.build_model()
with tf.Session() as dqn.tf_session:
dqn._init_tf()
print(dqn.tf_session.run(dqn.model, feed_dict={dqn.s_: g1}))
processes = []
for p in range(4):
processes.append(mp.Process(target=q_par, args=(dqn,g1)))
processes[p].start()
for p in range(4):
processes[p].join()
