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Suppress verbose output #77

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Suppress verbose output #77

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0809bca
suppress verbose print
CaioContiG May 12, 2025
a25c5f3
print info when done
CaioContiG May 12, 2025
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10 changes: 5 additions & 5 deletions turtlebot3_dqn/turtlebot3_dqn/dqn_agent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,7 +151,7 @@ def process(self):
while True:
local_step += 1

q_values = self.model.predict(state)
q_values = self.model.predict(state, verbose=0)
sum_max_q += float(numpy.max(q_values))

action = int(self.get_action(state))
Expand Down Expand Up @@ -246,9 +246,9 @@ def get_action(self, state):
if lucky > (1 - self.epsilon):
result = random.randint(0, self.action_size - 1)
else:
result = numpy.argmax(self.model.predict(state))
result = numpy.argmax(self.model.predict(state, verbose=0))
else:
result = numpy.argmax(self.model.predict(state))
result = numpy.argmax(self.model.predict(state, verbose=0))

return result

Expand Down Expand Up @@ -300,11 +300,11 @@ def train_model(self, terminal):

current_states = numpy.array([transition[0] for transition in data_in_mini_batch])
current_states = current_states.squeeze()
current_qvalues_list = self.model.predict(current_states)
current_qvalues_list = self.model.predict(current_states, verbose=0)

next_states = numpy.array([transition[3] for transition in data_in_mini_batch])
next_states = next_states.squeeze()
next_qvalues_list = self.target_model.predict(next_states)
next_qvalues_list = self.target_model.predict(next_states, verbose=0)

x_train = []
y_train = []
Expand Down
18 changes: 11 additions & 7 deletions turtlebot3_dqn/turtlebot3_dqn/dqn_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,6 @@ def process(self):
action = int(self.get_action(state))

req = Dqn.Request()
print(int(action))
req.action = action
req.init = init

Expand Down Expand Up @@ -136,7 +135,13 @@ def process(self):
'Exception while calling service: {0}'.format(future.exception()))

break

if done:
print(
'Episode:', episode,
'score:', score,
'memory length:', len(self.memory),
'epsilon:', self.epsilon)

time.sleep(0.01)

def build_model(self):
Expand All @@ -161,8 +166,7 @@ def get_action(self, state):
return random.randrange(self.action_size)
else:
state = numpy.asarray(state)
q_value = self.model.predict(state.reshape(1, len(state)))
print(numpy.argmax(q_value[0]))
q_value = self.model.predict(state.reshape(1, len(state)), verbose=0)
return numpy.argmax(q_value[0])

def train_model(self, target_train_start=False):
Expand All @@ -177,13 +181,13 @@ def train_model(self, target_train_start=False):
next_state = numpy.asarray(mini_batch[i][3])
done = numpy.asarray(mini_batch[i][4])

q_value = self.model.predict(state.reshape(1, len(state)))
q_value = self.model.predict(state.reshape(1, len(state)), verbose=0)
self.max_q_value = numpy.max(q_value)

if not target_train_start:
target_value = self.model.predict(next_state.reshape(1, len(next_state)))
target_value = self.model.predict(next_state.reshape(1, len(next_state)), verbose=0)
else:
target_value = self.target_model.predict(next_state.reshape(1, len(next_state)))
target_value = self.target_model.predict(next_state.reshape(1, len(next_state)), verbose=0)

if done:
next_q_value = reward
Expand Down