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EMADL2CPP
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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
2f61d2cc
Commit
2f61d2cc
authored
Jun 07, 2019
by
Nicola Gatto
Browse files
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Adapt tests
parent
0c8d4322
Pipeline
#148401
failed with stages
in 58 seconds
Changes
13
Pipelines
1
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13 changed files
with
185 additions
and
158 deletions
+185
-158
src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java
...java/de/monticore/lang/monticar/emadl/GenerationTest.java
+4
-2
src/test/java/de/monticore/lang/monticar/emadl/IntegrationCaffe2Test.java
.../monticore/lang/monticar/emadl/IntegrationCaffe2Test.java
+2
-0
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
...forcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
+22
-18
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/cnnarch_logger.py
...ntModel/cartpole/reinforcement_learning/cnnarch_logger.py
+16
-14
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/strategy.py
...orcementModel/cartpole/reinforcement_learning/strategy.py
+4
-4
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNTrainer_mountaincar_master_actor.py
...tModel/mountaincar/CNNTrainer_mountaincar_master_actor.py
+28
-24
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/cnnarch_logger.py
...odel/mountaincar/reinforcement_learning/cnnarch_logger.py
+16
-14
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/strategy.py
...ementModel/mountaincar/reinforcement_learning/strategy.py
+4
-4
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
...ementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
+27
-23
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/cnnarch_logger.py
...ementModel/torcs/reinforcement_learning/cnnarch_logger.py
+16
-14
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/environment.py
...orcementModel/torcs/reinforcement_learning/environment.py
+10
-10
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/strategy.py
...inforcementModel/torcs/reinforcement_learning/strategy.py
+4
-4
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/torcs_agent_dqn_reward_executor.py
...reinforcement_learning/torcs_agent_dqn_reward_executor.py
+32
-27
No files found.
src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java
View file @
2f61d2cc
...
...
@@ -268,13 +268,15 @@ public class GenerationTest extends AbstractSymtabTest {
"reinforcement_learning/environment.py"
,
"reinforcement_learning/replay_memory.py"
,
"reinforcement_learning/util.py"
,
"reinforcement_learning/cnnarch_logger.py"
,
"reinforcement_learning/torcs_agent_dqn_reward_executor.py"
"reinforcement_learning/cnnarch_logger.py"
)
);
assertTrue
(
Paths
.
get
(
"./target/generated-sources-emadl/reinforcement_learning/_torcs_agent_dqn_reward_executor.so"
)
.
toFile
().
exists
());
assertTrue
(
Paths
.
get
(
"./target/generated-sources-emadl/reinforcement_learning/torcs_agent_dqn_reward_executor.py"
)
.
toFile
().
exists
());
}
@Test
...
...
src/test/java/de/monticore/lang/monticar/emadl/IntegrationCaffe2Test.java
View file @
2f61d2cc
...
...
@@ -26,6 +26,7 @@ import de.monticore.lang.monticar.emadl.generator.EMADLGeneratorCli;
import
de.se_rwth.commons.logging.Log
;
import
freemarker.template.TemplateException
;
import
org.junit.Before
;
import
org.junit.Ignore
;
import
org.junit.Test
;
import
java.io.IOException
;
...
...
@@ -39,6 +40,7 @@ import java.util.List;
import
static
junit
.
framework
.
TestCase
.
assertTrue
;
import
static
org
.
junit
.
Assert
.
assertFalse
;
@Ignore
public
class
IntegrationCaffe2Test
extends
IntegrationTest
{
public
IntegrationCaffe2Test
()
{
super
(
"CAFFE2"
,
"39253EC049D4A4E5FA0536AD34874B9D#1DBAEE1B1BD83FB7CB5F70AE91B29638#13D139510DC5681639AA91D7250288D3#1A42D4842D0664937A9F6B727BD60CEF"
);
...
...
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
View file @
2f61d2cc
...
...
@@ -7,21 +7,21 @@ import CNNCreator_cartpole_master_dqn
import
os
import
sys
import
re
import
logging
import
time
import
numpy
as
np
import
mxnet
as
mx
def
resume_session
():
session_param_output
=
os
.
path
.
join
(
session_output_dir
,
agent_name
)
def
resume_session
(
sessions_dir
):
resume_session
=
False
resume_directory
=
None
if
os
.
path
.
isdir
(
session
_output_dir
)
and
os
.
path
.
isdir
(
session_param_output
):
if
os
.
path
.
isdir
(
session
s_dir
):
regex
=
re
.
compile
(
r
'\d\d\d\d-\d\d-\d\d-\d\d-\d\d'
)
dir_content
=
os
.
listdir
(
session
_param_output
)
dir_content
=
os
.
listdir
(
session
s_dir
)
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'
)
interrupted_session_dir
=
os
.
path
.
join
(
session
s_dir
,
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'
:
...
...
@@ -32,12 +32,13 @@ def resume_session():
if
__name__
==
"__main__"
:
agent_name
=
'cartpole_master_dqn'
agent_name
=
'cartpole_master_dqn'
# Prepare output directory and logger
output_directory
=
'model_output'
\
+
'/'
+
agent_name
\
+
'/'
+
time
.
strftime
(
'%Y-%m-%d-%H-%M-%S'
,
time
.
localtime
(
time
.
time
()))
all_output_dir
=
os
.
path
.
join
(
'model'
,
agent_name
)
output_directory
=
os
.
path
.
join
(
all_output_dir
,
time
.
strftime
(
'%Y-%m-%d-%H-%M-%S'
,
time
.
localtime
(
time
.
time
())))
ArchLogger
.
set_output_directory
(
output_directory
)
ArchLogger
.
set_logger_name
(
agent_name
)
ArchLogger
.
set_output_level
(
ArchLogger
.
INFO
)
...
...
@@ -51,12 +52,12 @@ if __name__ == "__main__":
agent_params
=
{
'environment'
:
env
,
'replay_memory_params'
:
{
'method'
:
'buffer'
,
'memory_size'
:
10000
,
'sample_size'
:
32
,
'state_dtype'
:
'float32'
,
'action_dtype'
:
'float32'
,
'rewards_dtype'
:
'float32'
'method'
:
'buffer'
,
'memory_size'
:
10000
,
'sample_size'
:
32
,
'state_dtype'
:
'float32'
,
'action_dtype'
:
'float32'
,
'rewards_dtype'
:
'float32'
},
'strategy_params'
:
{
'method'
:
'epsgreedy'
,
...
...
@@ -67,6 +68,7 @@ if __name__ == "__main__":
},
'agent_name'
:
agent_name
,
'verbose'
:
True
,
'output_directory'
:
output_directory
,
'state_dim'
:
(
4
,),
'action_dim'
:
(
2
,),
'ctx'
:
'cpu'
,
...
...
@@ -86,9 +88,11 @@ if __name__ == "__main__":
'double_dqn'
:
False
,
}
resume
,
resume_directory
=
resume_session
()
resume
,
resume_directory
=
resume_session
(
all_output_dir
)
if
resume
:
output_directory
,
_
=
os
.
path
.
split
(
resume_directory
)
ArchLogger
.
set_output_directory
(
output_directory
)
resume_agent_params
=
{
'session_dir'
:
resume_directory
,
'environment'
:
env
,
...
...
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/cnnarch_logger.py
View file @
2f61d2cc
...
...
@@ -52,22 +52,24 @@ class ArchLogger(object):
fmt
=
ArchLogger
.
__logformat
,
datefmt
=
ArchLogger
.
__dateformat
)
logger
=
logging
.
getLogger
(
ArchLogger
.
__logger_name
)
logger
.
setLevel
(
ArchLogger
.
__output_level
)
logger
.
propagate
=
False
stream_handler
=
logging
.
StreamHandler
(
sys
.
stdout
)
stream_handler
.
setLevel
(
ArchLogger
.
__output_level
)
stream_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
stream_handler
)
if
not
logger
.
handlers
:
logger
.
setLevel
(
ArchLogger
.
__output_level
)
stream_handler
=
logging
.
StreamHandler
(
sys
.
stdout
)
stream_handler
.
setLevel
(
ArchLogger
.
__output_level
)
stream_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
stream_handler
)
if
make_log_file
:
util
.
make_directory_if_not_exist
(
ArchLogger
.
__output_directory
)
log_file
=
os
.
path
.
join
(
ArchLogger
.
__output_directory
,
ArchLogger
.
__logger_name
+
'.log'
)
file_handler
=
logging
.
FileHandler
(
log_file
,
mode
=
filemode
)
file_handler
.
setLevel
(
ArchLogger
.
__output_level
)
file_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
file_handler
)
if
make_log_file
:
util
.
make_directory_if_not_exist
(
ArchLogger
.
__output_directory
)
log_file
=
os
.
path
.
join
(
ArchLogger
.
__output_directory
,
ArchLogger
.
__logger_name
+
'.log'
)
file_handler
=
logging
.
FileHandler
(
log_file
,
mode
=
filemode
)
file_handler
.
setLevel
(
ArchLogger
.
__output_level
)
file_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
file_handler
)
ArchLogger
.
_logger
=
logger
@
staticmethod
...
...
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/strategy.py
View file @
2f61d2cc
...
...
@@ -140,7 +140,7 @@ class OrnsteinUhlenbeckStrategy(BaseStrategy):
sigma
=
.
3
,
decay
=
NoDecay
()
):
super
(
OrnsteinUhlenbeckStrategy
,
self
).
__init__
()
super
(
OrnsteinUhlenbeckStrategy
,
self
).
__init__
(
decay
)
self
.
eps
=
eps
self
.
cur_eps
=
eps
...
...
@@ -150,9 +150,9 @@ class OrnsteinUhlenbeckStrategy(BaseStrategy):
self
.
_action_low
=
action_low
self
.
_action_high
=
action_high
self
.
_mu
=
mu
self
.
_theta
=
theta
self
.
_sigma
=
sigma
self
.
_mu
=
np
.
array
(
mu
)
self
.
_theta
=
np
.
array
(
theta
)
self
.
_sigma
=
np
.
array
(
sigma
)
self
.
state
=
np
.
ones
(
self
.
_action_dim
)
*
self
.
_mu
...
...
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNTrainer_mountaincar_master_actor.py
View file @
2f61d2cc
...
...
@@ -8,21 +8,21 @@ import CNNCreator_mountaincar_master_actor
import
os
import
sys
import
re
import
logging
import
time
import
numpy
as
np
import
mxnet
as
mx
def
resume_session
():
session_param_output
=
os
.
path
.
join
(
session_output_dir
,
agent_name
)
def
resume_session
(
sessions_dir
):
resume_session
=
False
resume_directory
=
None
if
os
.
path
.
isdir
(
session
_output_dir
)
and
os
.
path
.
isdir
(
session_param_output
):
if
os
.
path
.
isdir
(
session
s_dir
):
regex
=
re
.
compile
(
r
'\d\d\d\d-\d\d-\d\d-\d\d-\d\d'
)
dir_content
=
os
.
listdir
(
session
_param_output
)
dir_content
=
os
.
listdir
(
session
s_dir
)
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'
)
interrupted_session_dir
=
os
.
path
.
join
(
session
s_dir
,
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'
:
...
...
@@ -33,12 +33,13 @@ def resume_session():
if
__name__
==
"__main__"
:
agent_name
=
'mountaincar_master_actor'
agent_name
=
'mountaincar_master_actor'
# Prepare output directory and logger
output_directory
=
'model_output'
\
+
'/'
+
agent_name
\
+
'/'
+
time
.
strftime
(
'%Y-%m-%d-%H-%M-%S'
,
time
.
localtime
(
time
.
time
()))
all_output_dir
=
os
.
path
.
join
(
'model'
,
agent_name
)
output_directory
=
os
.
path
.
join
(
all_output_dir
,
time
.
strftime
(
'%Y-%m-%d-%H-%M-%S'
,
time
.
localtime
(
time
.
time
())))
ArchLogger
.
set_output_directory
(
output_directory
)
ArchLogger
.
set_logger_name
(
agent_name
)
ArchLogger
.
set_output_level
(
ArchLogger
.
INFO
)
...
...
@@ -48,18 +49,18 @@ if __name__ == "__main__":
context
=
mx
.
cpu
()
actor_creator
=
CNNCreator_mountaincar_master_actor
.
CNNCreator_mountaincar_master_actor
()
actor_creator
.
construct
(
context
)
critic_creator
=
CNNCreator_MountaincarCritic
.
CNNCreator_MountaincarCritic
()
critic_creator
=
CNNCreator_MountaincarCritic
()
critic_creator
.
construct
(
context
)
agent_params
=
{
'environment'
:
env
,
'replay_memory_params'
:
{
'method'
:
'buffer'
,
'memory_size'
:
1000000
,
'sample_size'
:
64
,
'state_dtype'
:
'float32'
,
'action_dtype'
:
'float32'
,
'rewards_dtype'
:
'float32'
'method'
:
'buffer'
,
'memory_size'
:
1000000
,
'sample_size'
:
64
,
'state_dtype'
:
'float32'
,
'action_dtype'
:
'float32'
,
'rewards_dtype'
:
'float32'
},
'strategy_params'
:
{
'method'
:
'ornstein_uhlenbeck'
,
...
...
@@ -67,14 +68,15 @@ if __name__ == "__main__":
'min_epsilon'
:
0.01
,
'epsilon_decay_method'
:
'linear'
,
'epsilon_decay'
:
0.01
,
'action_low'
:
-
1
'action_high'
:
1
'mu'
:
np
.
array
([
0
])
'theta'
:
np
.
array
([
0.15
])
'sigma'
:
np
.
array
([
0.3
])
'action_low'
:
-
1
,
'action_high'
:
1
,
'mu'
:
[
0
],
'theta'
:
[
0.15
],
'sigma'
:
[
0.3
],
},
'agent_name'
:
agent_name
,
'verbose'
:
True
,
'output_directory'
:
output_directory
,
'state_dim'
:
(
2
,),
'action_dim'
:
(
1
,),
'ctx'
:
'cpu'
,
...
...
@@ -93,9 +95,11 @@ if __name__ == "__main__":
'learning_rate'
:
0.001
},
}
resume
,
resume_directory
=
resume_session
()
resume
,
resume_directory
=
resume_session
(
all_output_dir
)
if
resume
:
output_directory
,
_
=
os
.
path
.
split
(
resume_directory
)
ArchLogger
.
set_output_directory
(
output_directory
)
resume_agent_params
=
{
'session_dir'
:
resume_directory
,
'environment'
:
env
,
...
...
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/cnnarch_logger.py
View file @
2f61d2cc
...
...
@@ -52,22 +52,24 @@ class ArchLogger(object):
fmt
=
ArchLogger
.
__logformat
,
datefmt
=
ArchLogger
.
__dateformat
)
logger
=
logging
.
getLogger
(
ArchLogger
.
__logger_name
)
logger
.
setLevel
(
ArchLogger
.
__output_level
)
logger
.
propagate
=
False
stream_handler
=
logging
.
StreamHandler
(
sys
.
stdout
)
stream_handler
.
setLevel
(
ArchLogger
.
__output_level
)
stream_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
stream_handler
)
if
not
logger
.
handlers
:
logger
.
setLevel
(
ArchLogger
.
__output_level
)
stream_handler
=
logging
.
StreamHandler
(
sys
.
stdout
)
stream_handler
.
setLevel
(
ArchLogger
.
__output_level
)
stream_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
stream_handler
)
if
make_log_file
:
util
.
make_directory_if_not_exist
(
ArchLogger
.
__output_directory
)
log_file
=
os
.
path
.
join
(
ArchLogger
.
__output_directory
,
ArchLogger
.
__logger_name
+
'.log'
)
file_handler
=
logging
.
FileHandler
(
log_file
,
mode
=
filemode
)
file_handler
.
setLevel
(
ArchLogger
.
__output_level
)
file_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
file_handler
)
if
make_log_file
:
util
.
make_directory_if_not_exist
(
ArchLogger
.
__output_directory
)
log_file
=
os
.
path
.
join
(
ArchLogger
.
__output_directory
,
ArchLogger
.
__logger_name
+
'.log'
)
file_handler
=
logging
.
FileHandler
(
log_file
,
mode
=
filemode
)
file_handler
.
setLevel
(
ArchLogger
.
__output_level
)
file_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
file_handler
)
ArchLogger
.
_logger
=
logger
@
staticmethod
...
...
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/strategy.py
View file @
2f61d2cc
...
...
@@ -140,7 +140,7 @@ class OrnsteinUhlenbeckStrategy(BaseStrategy):
sigma
=
.
3
,
decay
=
NoDecay
()
):
super
(
OrnsteinUhlenbeckStrategy
,
self
).
__init__
()
super
(
OrnsteinUhlenbeckStrategy
,
self
).
__init__
(
decay
)
self
.
eps
=
eps
self
.
cur_eps
=
eps
...
...
@@ -150,9 +150,9 @@ class OrnsteinUhlenbeckStrategy(BaseStrategy):
self
.
_action_low
=
action_low
self
.
_action_high
=
action_high
self
.
_mu
=
mu
self
.
_theta
=
theta
self
.
_sigma
=
sigma
self
.
_mu
=
np
.
array
(
mu
)
self
.
_theta
=
np
.
array
(
theta
)
self
.
_sigma
=
np
.
array
(
sigma
)
self
.
state
=
np
.
ones
(
self
.
_action_dim
)
*
self
.
_mu
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
View file @
2f61d2cc
...
...
@@ -7,21 +7,21 @@ import CNNCreator_torcs_agent_torcsAgent_dqn
import
os
import
sys
import
re
import
logging
import
time
import
numpy
as
np
import
mxnet
as
mx
def
resume_session
():
session_param_output
=
os
.
path
.
join
(
session_output_dir
,
agent_name
)
def
resume_session
(
sessions_dir
):
resume_session
=
False
resume_directory
=
None
if
os
.
path
.
isdir
(
session
_output_dir
)
and
os
.
path
.
isdir
(
session_param_output
):
if
os
.
path
.
isdir
(
session
s_dir
):
regex
=
re
.
compile
(
r
'\d\d\d\d-\d\d-\d\d-\d\d-\d\d'
)
dir_content
=
os
.
listdir
(
session
_param_output
)
dir_content
=
os
.
listdir
(
session
s_dir
)
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'
)
interrupted_session_dir
=
os
.
path
.
join
(
session
s_dir
,
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'
:
...
...
@@ -32,22 +32,23 @@ def resume_session():
if
__name__
==
"__main__"
:
agent_name
=
'torcs_agent_torcsAgent_dqn'
agent_name
=
'torcs_agent_torcsAgent_dqn'
# Prepare output directory and logger
output_directory
=
'model_output'
\
+
'/'
+
agent_name
\
+
'/'
+
time
.
strftime
(
'%Y-%m-%d-%H-%M-%S'
,
time
.
localtime
(
time
.
time
()))
all_output_dir
=
os
.
path
.
join
(
'model'
,
agent_name
)
output_directory
=
os
.
path
.
join
(
all_output_dir
,
time
.
strftime
(
'%Y-%m-%d-%H-%M-%S'
,
time
.
localtime
(
time
.
time
())))
ArchLogger
.
set_output_directory
(
output_directory
)
ArchLogger
.
set_logger_name
(
agent_name
)
ArchLogger
.
set_output_level
(
ArchLogger
.
INFO
)
env_params
=
{
'ros_node_name'
:
'torcs_agent_torcsAgent_dqnTrainerNode'
,
'state_topic'
:
'preprocessor_state'
,
'action_topic'
:
'postprocessor_action'
,
'reset_topic'
:
'torcs_reset'
,
'terminal_state_topic'
:
'prepocessor_is_terminal'
'ros_node_name'
:
'torcs_agent_torcsAgent_dqnTrainerNode'
,
'state_topic'
:
'preprocessor_state'
,
'action_topic'
:
'postprocessor_action'
,
'reset_topic'
:
'torcs_reset'
,
'terminal_state_topic'
:
'prepocessor_is_terminal'
,
}
env
=
reinforcement_learning
.
environment
.
RosEnvironment
(
**
env_params
)
...
...
@@ -58,12 +59,12 @@ if __name__ == "__main__":
agent_params
=
{
'environment'
:
env
,
'replay_memory_params'
:
{
'method'
:
'buffer'
,
'memory_size'
:
1000000
,
'sample_size'
:
32
,
'state_dtype'
:
'float32'
,
'action_dtype'
:
'float32'
,
'rewards_dtype'
:
'float32'
'method'
:
'buffer'
,
'memory_size'
:
1000000
,
'sample_size'
:
32
,
'state_dtype'
:
'float32'
,
'action_dtype'
:
'float32'
,
'rewards_dtype'
:
'float32'
},
'strategy_params'
:
{
'method'
:
'epsgreedy'
,
...
...
@@ -74,6 +75,7 @@ if __name__ == "__main__":
},
'agent_name'
:
agent_name
,
'verbose'
:
True
,
'output_directory'
:
output_directory
,
'state_dim'
:
(
5
,),
'action_dim'
:
(
30
,),
'ctx'
:
'cpu'
,
...
...
@@ -92,9 +94,11 @@ if __name__ == "__main__":
'double_dqn'
:
True
,
}
resume
,
resume_directory
=
resume_session
()
resume
,
resume_directory
=
resume_session
(
all_output_dir
)
if
resume
:
output_directory
,
_
=
os
.
path
.
split
(
resume_directory
)
ArchLogger
.
set_output_directory
(
output_directory
)
resume_agent_params
=
{
'session_dir'
:
resume_directory
,
'environment'
:
env
,
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/cnnarch_logger.py
View file @
2f61d2cc
...
...
@@ -52,22 +52,24 @@ class ArchLogger(object):
fmt
=
ArchLogger
.
__logformat
,
datefmt
=
ArchLogger
.
__dateformat
)
logger
=
logging
.
getLogger
(
ArchLogger
.
__logger_name
)
logger
.
setLevel
(
ArchLogger
.
__output_level
)
logger
.
propagate
=
False
stream_handler
=
logging
.
StreamHandler
(
sys
.
stdout
)
stream_handler
.
setLevel
(
ArchLogger
.
__output_level
)
stream_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
stream_handler
)
if
not
logger
.
handlers
:
logger
.
setLevel
(
ArchLogger
.
__output_level
)
stream_handler
=
logging
.
StreamHandler
(
sys
.
stdout
)
stream_handler
.
setLevel
(
ArchLogger
.
__output_level
)
stream_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
stream_handler
)
if
make_log_file
:
util
.
make_directory_if_not_exist
(
ArchLogger
.
__output_directory
)
log_file
=
os
.
path
.
join
(
ArchLogger
.
__output_directory
,
ArchLogger
.
__logger_name
+
'.log'
)
file_handler
=
logging
.
FileHandler
(
log_file
,
mode
=
filemode
)
file_handler
.
setLevel
(
ArchLogger
.
__output_level
)
file_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
file_handler
)
if
make_log_file
:
util
.
make_directory_if_not_exist
(
ArchLogger
.
__output_directory
)
log_file
=
os
.
path
.
join
(
ArchLogger
.
__output_directory
,
ArchLogger
.
__logger_name
+
'.log'
)
file_handler
=
logging
.
FileHandler
(
log_file
,
mode
=
filemode
)
file_handler
.
setLevel
(
ArchLogger
.
__output_level
)
file_handler
.
setFormatter
(
formatter
)
logger
.
addHandler
(
file_handler
)
ArchLogger
.
_logger
=
logger
@
staticmethod
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/environment.py
View file @
2f61d2cc
...
...
@@ -10,9 +10,11 @@ class RewardFunction(object):
self
.
__reward_wrapper
.
init
()
def
reward
(
self
,
state
,
terminal
):
s
=
state
.
astype
(
'double'
)
t
=
bool
(
terminal
)
inp
=
torcs_agent_dqn_reward_executor
.
torcs_agent_dqn_reward_input
()
inp
.
state
=
s
tate
inp
.
isTerminal
=
t
erminal
inp
.
state
=
s
inp
.
isTerminal
=
t
output
=
self
.
__reward_wrapper
.
execute
(
inp
)
return
output
.
reward
...
...
@@ -40,7 +42,7 @@ import rospy
import
thread
import
numpy
as
np
import
time
from
std_msgs.msg
import
Float32MultiArray
,
Bool
,
Int32
,
MultiArrayDimension
from
std_msgs.msg
import
Float32MultiArray
,
Bool
,
Int32
,
MultiArrayDimension
,
Float32
class
RosEnvironment
(
Environment
):
def
__init__
(
self
,
...
...
@@ -50,15 +52,13 @@ class RosEnvironment(Environment):
action_topic
=
'action'
,
reset_topic
=
'reset'
,
terminal_state_topic
=
'terminal'
,
meta_topic
=
'meta'
,
greeting_topic
=
'greeting'
):
reward_topic
=
'reward'
):
super
(
RosEnvironment
,
self
).
__init__
()
self
.
__timeout_in_s
=
timeout_in_s
self
.
__waiting_for_state_update
=
False
self
.
__waiting_for_terminal_update
=
False
self
.
__last_received_state
=
0
self
.
__last_received_terminal
=
0
self
.
__last_received_terminal
=
True
rospy
.
loginfo
(
"Initialize node {0}"
.
format
(
ros_node_name
))
...
...
@@ -111,7 +111,8 @@ class RosEnvironment(Environment):
def
__wait_for_new_state
(
self
,
publisher
,
msg
):
time_of_timeout
=
time
.
time
()
+
self
.
__timeout_in_s
timeout_counter
=
0
while
(
self
.
__waiting_for_state_update
or
self
.
__waiting_for_terminal_update
):
while
(
self
.
__waiting_for_state_update
or
self
.
__waiting_for_terminal_update
):
is_timeout
=
(
time
.
time
()
>
time_of_timeout
)
if
(
is_timeout
):
if
timeout_counter
<
3
:
...
...
@@ -127,9 +128,8 @@ class RosEnvironment(Environment):
def
close
(
self
):
rospy
.
signal_shutdown
(
'Program ended!'
)
def
__state_callback
(
self
,
data
):
self
.
__last_received_state
=
np
.
array
(
data
.
data
,
dtype
=
'
double
'
)
self
.
__last_received_state
=
np
.
array
(
data
.
data
,
dtype
=
'
float32
'
)
rospy
.
logdebug
(
'Received state: {}'
.
format
(
self
.
__last_received_state
))
self
.
__waiting_for_state_update
=
False
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/strategy.py
View file @
2f61d2cc
...
...
@@ -140,7 +140,7 @@ class OrnsteinUhlenbeckStrategy(BaseStrategy):
sigma
=
.
3
,
decay
=
NoDecay
()
):
super
(
OrnsteinUhlenbeckStrategy
,
self
).
__init__
()
super
(
OrnsteinUhlenbeckStrategy
,
self
).
__init__
(
decay
)
self
.
eps
=
eps
self
.
cur_eps
=
eps
...
...
@@ -150,9 +150,9 @@ class OrnsteinUhlenbeckStrategy(BaseStrategy):
self
.
_action_low
=
action_low
self
.
_action_high
=
action_high
self
.
_mu
=
mu
self
.
_theta
=
theta
self
.
_sigma
=
sigma
self
.
_mu
=
np
.
array
(
mu
)