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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
cdc08ba5
Commit
cdc08ba5
authored
May 16, 2019
by
Nicola Gatto
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Merge branch 'integrate-reinforcement-gluon' into annotate-architecture
parents
cbf77848
62786f93
Changes
10
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10 changed files
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214 additions
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123 deletions
+214
-123
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
...forcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
+67
-30
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/agent.py
...inforcementModel/cartpole/reinforcement_learning/agent.py
+12
-9
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/environment.py
...ementModel/cartpole/reinforcement_learning/environment.py
+4
-0
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/util.py
...einforcementModel/cartpole/reinforcement_learning/util.py
+8
-2
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
...ementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
+67
-30
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/agent.py
.../reinforcementModel/torcs/reinforcement_learning/agent.py
+12
-9
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/environment.py
...orcementModel/torcs/reinforcement_learning/environment.py
+9
-1
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/torcs_agent_dqn_reward_executor.py
...reinforcement_learning/torcs_agent_dqn_reward_executor.py
+27
-32
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/util.py
...n/reinforcementModel/torcs/reinforcement_learning/util.py
+8
-2
src/test/resources/target_code/gluon/reinforcementModel/torcs/start_training.sh
...get_code/gluon/reinforcementModel/torcs/start_training.sh
+0
-8
No files found.
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
View file @
cdc08ba5
from
reinforcement_learning.agent
import
DqnAgent
from
reinforcement_learning.util
import
AgentSignalHandler
import
reinforcement_learning.environment
import
CNNCreator_cartpole_master_dqn
import
os
import
sys
import
re
import
logging
import
mxnet
as
mx
session_output_dir
=
'session'
agent_name
=
'cartpole_master_dqn'
session_param_output
=
os
.
path
.
join
(
session_output_dir
,
agent_name
)
def
resume_session
():
session_param_output
=
os
.
path
.
join
(
session_output_dir
,
agent_name
)
resume_session
=
False
resume_directory
=
None
if
os
.
path
.
isdir
(
session_output_dir
)
and
os
.
path
.
isdir
(
session_param_output
):
regex
=
re
.
compile
(
r
'\d\d\d\d-\d\d-\d\d-\d\d-\d\d'
)
dir_content
=
os
.
listdir
(
session_param_output
)
session_files
=
filter
(
regex
.
search
,
dir_content
)
session_files
.
sort
(
reverse
=
True
)
for
d
in
session_files
:
interrupted_session_dir
=
os
.
path
.
join
(
session_param_output
,
d
,
'.interrupted_session'
)
if
os
.
path
.
isdir
(
interrupted_session_dir
):
resume
=
raw_input
(
'Interrupted session from {} found. Do you want to resume? (y/n) '
.
format
(
d
))
if
resume
==
'y'
:
resume_session
=
True
resume_directory
=
interrupted_session_dir
break
return
resume_session
,
resume_directory
if
__name__
==
"__main__"
:
env
=
reinforcement_learning
.
environment
.
GymEnvironment
(
'CartPole-v0'
)
context
=
mx
.
cpu
()
...
...
@@ -28,32 +55,42 @@ if __name__ == "__main__":
'epsilon_decay'
:
0.01
,
}
agent
=
DqnAgent
(
network
=
net_creator
.
net
,
environment
=
env
,
replay_memory_params
=
replay_memory_params
,
policy_params
=
policy_params
,
state_dim
=
net_creator
.
get_input_shapes
()[
0
],
ctx
=
'cpu'
,
discount_factor
=
0.999
,
loss_function
=
'euclidean'
,
optimizer
=
'rmsprop'
,
optimizer_params
=
{
'learning_rate'
:
0.001
},
training_episodes
=
160
,
train_interval
=
1
,
use_fix_target
=
True
,
target_update_interval
=
200
,
double_dqn
=
False
,
snapshot_interval
=
20
,
agent_name
=
'cartpole_master_dqn'
,
max_episode_step
=
250
,
output_directory
=
'model'
,
verbose
=
True
,
live_plot
=
True
,
make_logfile
=
True
,
target_score
=
185.5
)
train_successfull
=
agent
.
train
()
agent
.
save_best_network
(
net_creator
.
_model_dir_
+
net_creator
.
_model_prefix_
+
'_newest'
,
epoch
=
0
)
\ No newline at end of file
resume_session
,
resume_directory
=
resume_session
()
if
resume_session
:
agent
=
DqnAgent
.
resume_from_session
(
resume_directory
,
net_creator
.
net
,
env
)
else
:
agent
=
DqnAgent
(
network
=
net_creator
.
net
,
environment
=
env
,
replay_memory_params
=
replay_memory_params
,
policy_params
=
policy_params
,
state_dim
=
net_creator
.
get_input_shapes
()[
0
],
ctx
=
'cpu'
,
discount_factor
=
0.999
,
loss_function
=
'euclidean'
,
optimizer
=
'rmsprop'
,
optimizer_params
=
{
'learning_rate'
:
0.001
},
training_episodes
=
160
,
train_interval
=
1
,
use_fix_target
=
True
,
target_update_interval
=
200
,
double_dqn
=
False
,
snapshot_interval
=
20
,
agent_name
=
agent_name
,
max_episode_step
=
250
,
output_directory
=
session_output_dir
,
verbose
=
True
,
live_plot
=
True
,
make_logfile
=
True
,
target_score
=
185.5
)
signal_handler
=
AgentSignalHandler
()
signal_handler
.
register_agent
(
agent
)
train_successful
=
agent
.
train
()
if
train_successful
:
agent
.
save_best_network
(
net_creator
.
_model_dir_
+
net_creator
.
_model_prefix_
+
'_newest'
,
epoch
=
0
)
\ No newline at end of file
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/agent.py
View file @
cdc08ba5
...
...
@@ -100,7 +100,7 @@ class DqnAgent(object):
# Prepare output directory and logger
self
.
__output_directory
=
output_directory
\
+
'/'
+
self
.
__agent_name
\
+
'/'
+
time
.
strftime
(
'%
d-%m-%Y
-%H-%M-%S'
,
time
.
localtime
(
self
.
__creation_time
))
+
'/'
+
time
.
strftime
(
'%
Y-%m-%d
-%H-%M-%S'
,
time
.
localtime
(
self
.
__creation_time
))
self
.
__logger
=
self
.
__setup_logging
()
self
.
__logger
.
info
(
'Agent created with following parameters: {}'
.
format
(
self
.
__make_config_dict
()))
...
...
@@ -113,9 +113,8 @@ class DqnAgent(object):
return
cls
(
network
,
environment
,
ctx
=
ctx
,
**
config_dict
)
@
classmethod
def
resume_from_session
(
cls
,
session_dir
,
net
work_type
):
def
resume_from_session
(
cls
,
session_dir
,
net
,
environment
):
import
pickle
session_dir
=
os
.
path
.
join
(
session_dir
,
'.interrupted_session'
)
if
not
os
.
path
.
exists
(
session_dir
):
raise
ValueError
(
'Session directory does not exist'
)
...
...
@@ -132,13 +131,14 @@ class DqnAgent(object):
with
open
(
files
[
'agent'
],
'rb'
)
as
f
:
agent
=
pickle
.
load
(
f
)
agent
.
__qnet
=
network_type
()
agent
.
__environment
=
environment
agent
.
__qnet
=
net
agent
.
__qnet
.
load_parameters
(
files
[
'q_net_params'
],
agent
.
__ctx
)
agent
.
__qnet
.
hybridize
()
agent
.
__qnet
(
nd
.
ones
((
1
,)
+
agent
.
__environment
.
state_dim
))
agent
.
__best_net
=
network_type
(
)
agent
.
__qnet
(
nd
.
random_normal
(
shape
=
((
1
,)
+
agent
.
__state_dim
),
ctx
=
agent
.
__ctx
))
agent
.
__best_net
=
copy_net
(
agent
.
__qnet
,
agent
.
__state_dim
,
agent
.
__ctx
)
agent
.
__best_net
.
load_parameters
(
files
[
'best_net_params'
],
agent
.
__ctx
)
agent
.
__target_qnet
=
network_type
(
)
agent
.
__target_qnet
=
copy_net
(
agent
.
__qnet
,
agent
.
__state_dim
,
agent
.
__ctx
)
agent
.
__target_qnet
.
load_parameters
(
files
[
'target_net_params'
],
agent
.
__ctx
)
agent
.
__logger
=
agent
.
__setup_logging
(
append
=
True
)
...
...
@@ -157,6 +157,8 @@ class DqnAgent(object):
del
self
.
__training_stats
.
logger
logger
=
self
.
__logger
self
.
__logger
=
None
self
.
__environment
.
close
()
self
.
__environment
=
None
self
.
__save_net
(
self
.
__qnet
,
'qnet'
,
session_dir
)
self
.
__qnet
=
None
...
...
@@ -169,7 +171,7 @@ class DqnAgent(object):
with
open
(
agent_session_file
,
'wb'
)
as
f
:
pickle
.
dump
(
self
,
f
)
self
.
__logger
=
logger
logger
.
info
(
'State successfully stored'
)
@
property
...
...
@@ -293,7 +295,7 @@ class DqnAgent(object):
return
loss
def
__do_snapshot_if_in_interval
(
self
,
episode
):
do_snapshot
=
(
episode
%
self
.
__snapshot_interval
==
0
)
do_snapshot
=
(
episode
!=
0
and
(
episode
%
self
.
__snapshot_interval
==
0
)
)
if
do_snapshot
:
self
.
save_parameters
(
episode
=
episode
)
self
.
__evaluate
()
...
...
@@ -318,6 +320,7 @@ class DqnAgent(object):
# Implementation Deep Q Learning described by Mnih et. al. in Playing Atari with Deep Reinforcement Learning
while
self
.
__current_episode
<
episodes
:
# Check interrupt flag
if
self
.
__interrupt_flag
:
self
.
__interrupt_flag
=
False
self
.
__interrupt_training
()
...
...
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/environment.py
View file @
cdc08ba5
...
...
@@ -17,6 +17,10 @@ class Environment:
def
step
(
self
,
action
):
pass
@
abc
.
abstractmethod
def
close
(
self
):
pass
import
gym
class
GymEnvironment
(
Environment
):
def
__init__
(
self
,
env_name
,
**
kwargs
):
...
...
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/util.py
View file @
cdc08ba5
...
...
@@ -37,13 +37,19 @@ class AgentSignalHandler(object):
def
__init__
(
self
):
signal
.
signal
(
signal
.
SIGINT
,
self
.
interrupt_training
)
self
.
__agent
=
None
self
.
__times_interrupted
=
0
def
register_agent
(
self
,
agent
):
self
.
__agent
=
agent
def
interrupt_training
(
self
,
sig
,
frame
):
if
self
.
__agent
:
self
.
__agent
.
set_interrupt_flag
(
True
)
self
.
__times_interrupted
=
self
.
__times_interrupted
+
1
if
self
.
__times_interrupted
<=
3
:
if
self
.
__agent
:
self
.
__agent
.
set_interrupt_flag
(
True
)
else
:
print
(
'Interrupt called three times: Force quit'
)
sys
.
exit
(
1
)
style
.
use
(
'fivethirtyeight'
)
class
TrainingStats
(
object
):
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
View file @
cdc08ba5
from
reinforcement_learning.agent
import
DqnAgent
from
reinforcement_learning.util
import
AgentSignalHandler
import
reinforcement_learning.environment
import
CNNCreator_torcs_agent_torcsAgent_dqn
import
os
import
sys
import
re
import
logging
import
mxnet
as
mx
session_output_dir
=
'session'
agent_name
=
'torcs_agent_torcsAgent_dqn'
session_param_output
=
os
.
path
.
join
(
session_output_dir
,
agent_name
)
def
resume_session
():
session_param_output
=
os
.
path
.
join
(
session_output_dir
,
agent_name
)
resume_session
=
False
resume_directory
=
None
if
os
.
path
.
isdir
(
session_output_dir
)
and
os
.
path
.
isdir
(
session_param_output
):
regex
=
re
.
compile
(
r
'\d\d\d\d-\d\d-\d\d-\d\d-\d\d'
)
dir_content
=
os
.
listdir
(
session_param_output
)
session_files
=
filter
(
regex
.
search
,
dir_content
)
session_files
.
sort
(
reverse
=
True
)
for
d
in
session_files
:
interrupted_session_dir
=
os
.
path
.
join
(
session_param_output
,
d
,
'.interrupted_session'
)
if
os
.
path
.
isdir
(
interrupted_session_dir
):
resume
=
raw_input
(
'Interrupted session from {} found. Do you want to resume? (y/n) '
.
format
(
d
))
if
resume
==
'y'
:
resume_session
=
True
resume_directory
=
interrupted_session_dir
break
return
resume_session
,
resume_directory
if
__name__
==
"__main__"
:
env_params
=
{
'ros_node_name'
:
'torcs_agent_torcsAgent_dqnTrainerNode'
,
...
...
@@ -35,32 +62,42 @@ if __name__ == "__main__":
'epsilon_decay'
:
0.0001
,
}
agent
=
DqnAgent
(
network
=
net_creator
.
net
,
environment
=
env
,
replay_memory_params
=
replay_memory_params
,
policy_params
=
policy_params
,
state_dim
=
net_creator
.
get_input_shapes
()[
0
],
ctx
=
'cpu'
,
discount_factor
=
0.999
,
loss_function
=
'euclidean'
,
optimizer
=
'rmsprop'
,
optimizer_params
=
{
'learning_rate'
:
0.001
},
training_episodes
=
20000
,
train_interval
=
1
,
use_fix_target
=
True
,
target_update_interval
=
500
,
double_dqn
=
True
,
snapshot_interval
=
1000
,
agent_name
=
'torcs_agent_torcsAgent_dqn'
,
max_episode_step
=
999999999
,
output_directory
=
'model'
,
verbose
=
True
,
live_plot
=
True
,
make_logfile
=
True
,
target_score
=
None
)
train_successfull
=
agent
.
train
()
agent
.
save_best_network
(
net_creator
.
_model_dir_
+
net_creator
.
_model_prefix_
+
'_newest'
,
epoch
=
0
)
\ No newline at end of file
resume_session
,
resume_directory
=
resume_session
()
if
resume_session
:
agent
=
DqnAgent
.
resume_from_session
(
resume_directory
,
net_creator
.
net
,
env
)
else
:
agent
=
DqnAgent
(
network
=
net_creator
.
net
,
environment
=
env
,
replay_memory_params
=
replay_memory_params
,
policy_params
=
policy_params
,
state_dim
=
net_creator
.
get_input_shapes
()[
0
],
ctx
=
'cpu'
,
discount_factor
=
0.999
,
loss_function
=
'euclidean'
,
optimizer
=
'rmsprop'
,
optimizer_params
=
{
'learning_rate'
:
0.001
},
training_episodes
=
20000
,
train_interval
=
1
,
use_fix_target
=
True
,
target_update_interval
=
500
,
double_dqn
=
True
,
snapshot_interval
=
1000
,
agent_name
=
agent_name
,
max_episode_step
=
999999999
,
output_directory
=
session_output_dir
,
verbose
=
True
,
live_plot
=
True
,
make_logfile
=
True
,
target_score
=
None
)
signal_handler
=
AgentSignalHandler
()
signal_handler
.
register_agent
(
agent
)
train_successful
=
agent
.
train
()
if
train_successful
:
agent
.
save_best_network
(
net_creator
.
_model_dir_
+
net_creator
.
_model_prefix_
+
'_newest'
,
epoch
=
0
)
\ No newline at end of file
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/agent.py
View file @
cdc08ba5
...
...
@@ -100,7 +100,7 @@ class DqnAgent(object):
# Prepare output directory and logger
self
.
__output_directory
=
output_directory
\
+
'/'
+
self
.
__agent_name
\
+
'/'
+
time
.
strftime
(
'%
d-%m-%Y
-%H-%M-%S'
,
time
.
localtime
(
self
.
__creation_time
))
+
'/'
+
time
.
strftime
(
'%
Y-%m-%d
-%H-%M-%S'
,
time
.
localtime
(
self
.
__creation_time
))
self
.
__logger
=
self
.
__setup_logging
()
self
.
__logger
.
info
(
'Agent created with following parameters: {}'
.
format
(
self
.
__make_config_dict
()))
...
...
@@ -113,9 +113,8 @@ class DqnAgent(object):
return
cls
(
network
,
environment
,
ctx
=
ctx
,
**
config_dict
)
@
classmethod
def
resume_from_session
(
cls
,
session_dir
,
net
work_type
):
def
resume_from_session
(
cls
,
session_dir
,
net
,
environment
):
import
pickle
session_dir
=
os
.
path
.
join
(
session_dir
,
'.interrupted_session'
)
if
not
os
.
path
.
exists
(
session_dir
):
raise
ValueError
(
'Session directory does not exist'
)
...
...
@@ -132,13 +131,14 @@ class DqnAgent(object):
with
open
(
files
[
'agent'
],
'rb'
)
as
f
:
agent
=
pickle
.
load
(
f
)
agent
.
__qnet
=
network_type
()
agent
.
__environment
=
environment
agent
.
__qnet
=
net
agent
.
__qnet
.
load_parameters
(
files
[
'q_net_params'
],
agent
.
__ctx
)
agent
.
__qnet
.
hybridize
()
agent
.
__qnet
(
nd
.
ones
((
1
,)
+
agent
.
__environment
.
state_dim
))
agent
.
__best_net
=
network_type
(
)
agent
.
__qnet
(
nd
.
random_normal
(
shape
=
((
1
,)
+
agent
.
__state_dim
),
ctx
=
agent
.
__ctx
))
agent
.
__best_net
=
copy_net
(
agent
.
__qnet
,
agent
.
__state_dim
,
agent
.
__ctx
)
agent
.
__best_net
.
load_parameters
(
files
[
'best_net_params'
],
agent
.
__ctx
)
agent
.
__target_qnet
=
network_type
(
)
agent
.
__target_qnet
=
copy_net
(
agent
.
__qnet
,
agent
.
__state_dim
,
agent
.
__ctx
)
agent
.
__target_qnet
.
load_parameters
(
files
[
'target_net_params'
],
agent
.
__ctx
)
agent
.
__logger
=
agent
.
__setup_logging
(
append
=
True
)
...
...
@@ -157,6 +157,8 @@ class DqnAgent(object):
del
self
.
__training_stats
.
logger
logger
=
self
.
__logger
self
.
__logger
=
None
self
.
__environment
.
close
()
self
.
__environment
=
None
self
.
__save_net
(
self
.
__qnet
,
'qnet'
,
session_dir
)
self
.
__qnet
=
None
...
...
@@ -169,7 +171,7 @@ class DqnAgent(object):
with
open
(
agent_session_file
,
'wb'
)
as
f
:
pickle
.
dump
(
self
,
f
)
self
.
__logger
=
logger
logger
.
info
(
'State successfully stored'
)
@
property
...
...
@@ -293,7 +295,7 @@ class DqnAgent(object):
return
loss
def
__do_snapshot_if_in_interval
(
self
,
episode
):
do_snapshot
=
(
episode
%
self
.
__snapshot_interval
==
0
)
do_snapshot
=
(
episode
!=
0
and
(
episode
%
self
.
__snapshot_interval
==
0
)
)
if
do_snapshot
:
self
.
save_parameters
(
episode
=
episode
)
self
.
__evaluate
()
...
...
@@ -318,6 +320,7 @@ class DqnAgent(object):
# Implementation Deep Q Learning described by Mnih et. al. in Playing Atari with Deep Reinforcement Learning
while
self
.
__current_episode
<
episodes
:
# Check interrupt flag
if
self
.
__interrupt_flag
:
self
.
__interrupt_flag
=
False
self
.
__interrupt_training
()
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/environment.py
View file @
cdc08ba5
...
...
@@ -32,6 +32,10 @@ class Environment:
def
step
(
self
,
action
):
pass
@
abc
.
abstractmethod
def
close
(
self
):
pass
import
rospy
import
thread
import
numpy
as
np
...
...
@@ -83,7 +87,8 @@ class RosEnvironment(Environment):
reset_message
.
data
=
True
self
.
__waiting_for_state_update
=
True
self
.
__reset_publisher
.
publish
(
reset_message
)
self
.
__wait_for_new_state
(
self
.
__reset_publisher
,
reset_message
)
while
self
.
__last_received_terminal
:
self
.
__wait_for_new_state
(
self
.
__reset_publisher
,
reset_message
)
return
self
.
__last_received_state
def
step
(
self
,
action
):
...
...
@@ -119,6 +124,9 @@ class RosEnvironment(Environment):
exit
()
time
.
sleep
(
100
/
1000
)
def
close
(
self
):
rospy
.
signal_shutdown
(
'Program ended!'
)
def
__state_callback
(
self
,
data
):
self
.
__last_received_state
=
np
.
array
(
data
.
data
,
dtype
=
'double'
)
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/torcs_agent_dqn_reward_executor.py
View file @
cdc08ba5
# This file was automatically generated by SWIG (http://www.swig.org).
# Version 3.0.
12
# Version 3.0.
8
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from
sys
import
version_info
as
_swig_python_version_info
if
_swig_python_version_info
>=
(
2
,
7
,
0
):
def
swig_import_helper
():
import
importlib
pkg
=
__name__
.
rpartition
(
'.'
)[
0
]
mname
=
'.'
.
join
((
pkg
,
'_torcs_agent_dqn_reward_executor'
)).
lstrip
(
'.'
)
try
:
return
importlib
.
import_module
(
mname
)
except
ImportError
:
return
importlib
.
import_module
(
'_torcs_agent_dqn_reward_executor'
)
_torcs_agent_dqn_reward_executor
=
swig_import_helper
()
del
swig_import_helper
elif
_swig_python_version_info
>=
(
2
,
6
,
0
):
from
sys
import
version_info
if
version_info
>=
(
2
,
6
,
0
):
def
swig_import_helper
():
from
os.path
import
dirname
import
imp
...
...
@@ -26,27 +19,22 @@ elif _swig_python_version_info >= (2, 6, 0):
except
ImportError
:
import
_torcs_agent_dqn_reward_executor
return
_torcs_agent_dqn_reward_executor
try
:
_mod
=
imp
.
load_module
(
'_torcs_agent_dqn_reward_executor'
,
fp
,
pathname
,
description
)
finally
:
if
fp
is
not
None
:
if
fp
is
not
None
:
try
:
_mod
=
imp
.
load_module
(
'_torcs_agent_dqn_reward_executor'
,
fp
,
pathname
,
description
)
finally
:
fp
.
close
()
return
_mod
return
_mod
_torcs_agent_dqn_reward_executor
=
swig_import_helper
()
del
swig_import_helper
else
:
import
_torcs_agent_dqn_reward_executor
del
_swig_python_version_info
del
version_info
try
:
_swig_property
=
property
except
NameError
:
pass
# Python < 2.2 doesn't have 'property'.
try
:
import
builtins
as
__builtin__
except
ImportError
:
import
__builtin__
def
_swig_setattr_nondynamic
(
self
,
class_type
,
name
,
value
,
static
=
1
):
if
(
name
==
"thisown"
):
...
...
@@ -71,30 +59,37 @@ def _swig_setattr(self, class_type, name, value):
return
_swig_setattr_nondynamic
(
self
,
class_type
,
name
,
value
,
0
)
def
_swig_getattr
(
self
,
class_type
,
name
):
def
_swig_getattr
_nondynamic
(
self
,
class_type
,
name
,
static
=
1
):
if
(
name
==
"thisown"
):
return
self
.
this
.
own
()
method
=
class_type
.
__swig_getmethods__
.
get
(
name
,
None
)
if
method
:
return
method
(
self
)
raise
AttributeError
(
"'%s' object has no attribute '%s'"
%
(
class_type
.
__name__
,
name
))
if
(
not
static
):
return
object
.
__getattr__
(
self
,
name
)
else
:
raise
AttributeError
(
name
)
def
_swig_getattr
(
self
,
class_type
,
name
):
return
_swig_getattr_nondynamic
(
self
,
class_type
,
name
,
0
)
def
_swig_repr
(
self
):
try
:
strthis
=
"proxy of "
+
self
.
this
.
__repr__
()
except
__builtin__
.
Exception
:
except
Exception
:
strthis
=
""
return
"<%s.%s; %s >"
%
(
self
.
__class__
.
__module__
,
self
.
__class__
.
__name__
,
strthis
,)
try
:
_object
=
object
_newclass
=
1
except
__builtin__
.
Exception
:
except
AttributeError
:
class
_object
:
pass
_newclass
=
0
class
torcs_agent_dqn_reward_input
(
_object
):
__swig_setmethods__
=
{}
__setattr__
=
lambda
self
,
name
,
value
:
_swig_setattr
(
self
,
torcs_agent_dqn_reward_input
,
name
,
value
)
...
...
@@ -114,7 +109,7 @@ class torcs_agent_dqn_reward_input(_object):
this
=
_torcs_agent_dqn_reward_executor
.
new_torcs_agent_dqn_reward_input
()
try
:
self
.
this
.
append
(
this
)
except
__builtin__
.
Exception
:
except
Exception
:
self
.
this
=
this
__swig_destroy__
=
_torcs_agent_dqn_reward_executor
.
delete_torcs_agent_dqn_reward_input
__del__
=
lambda
self
:
None
...
...
@@ -136,7 +131,7 @@ class torcs_agent_dqn_reward_output(_object):
this
=
_torcs_agent_dqn_reward_executor
.
new_torcs_agent_dqn_reward_output
()
try
:
self
.
this
.
append
(
this
)
except
__builtin__
.
Exception
:
except
Exception
:
self
.
this
=
this
__swig_destroy__
=
_torcs_agent_dqn_reward_executor
.
delete_torcs_agent_dqn_reward_output
__del__
=
lambda
self
:
None
...
...
@@ -160,7 +155,7 @@ class torcs_agent_dqn_reward_executor(_object):
this
=
_torcs_agent_dqn_reward_executor
.
new_torcs_agent_dqn_reward_executor
()
try
:
self
.
this
.
append
(
this
)
except
__builtin__
.
Exception
:
except
Exception
:
self
.
this
=
this
__swig_destroy__
=
_torcs_agent_dqn_reward_executor
.
delete_torcs_agent_dqn_reward_executor
__del__
=
lambda
self
:
None
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/util.py
View file @
cdc08ba5
...
...
@@ -37,13 +37,19 @@ class AgentSignalHandler(object):
def
__init__
(
self
):
signal
.
signal
(
signal
.
SIGINT
,
self
.
interrupt_training
)
self
.
__agent
=
None
self
.
__times_interrupted
=
0
def
register_agent
(
self
,
agent
):
self
.
__agent
=
agent
<