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EMADL2CPP
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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
a51b42b6
Commit
a51b42b6
authored
May 30, 2019
by
Nicola Gatto
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48 changed files
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5682 additions
and
1205 deletions
+5682
-1205
src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java
...java/de/monticore/lang/monticar/emadl/GenerationTest.java
+33
-3
src/test/resources/models/reinforcementModel/cartpole/agent/CartPoleDQN.cnnt
...models/reinforcementModel/cartpole/agent/CartPoleDQN.cnnt
+1
-1
src/test/resources/models/reinforcementModel/mountaincar/agent/MountaincarActor.cnnt
...einforcementModel/mountaincar/agent/MountaincarActor.cnnt
+11
-6
src/test/resources/models/reinforcementModel/mountaincar/agent/MountaincarCritic.cnna
...inforcementModel/mountaincar/agent/MountaincarCritic.cnna
+1
-4
src/test/resources/models/reinforcementModel/torcs/agent/dqn/TorcsDQN.cnnt
...s/models/reinforcementModel/torcs/agent/dqn/TorcsDQN.cnnt
+1
-1
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNDataLoader_cartpole_master_dqn.py
...cementModel/cartpole/CNNDataLoader_cartpole_master_dqn.py
+57
-0
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
...forcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
+61
-50
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/action_policy.py
...entModel/cartpole/reinforcement_learning/action_policy.py
+0
-73
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/agent.py
...inforcementModel/cartpole/reinforcement_learning/agent.py
+769
-375
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/cnnarch_logger.py
...ntModel/cartpole/reinforcement_learning/cnnarch_logger.py
+93
-0
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/environment.py
...ementModel/cartpole/reinforcement_learning/environment.py
+0
-7
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/replay_memory.py
...entModel/cartpole/reinforcement_learning/replay_memory.py
+95
-42
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/strategy.py
...orcementModel/cartpole/reinforcement_learning/strategy.py
+172
-0
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/util.py
...einforcementModel/cartpole/reinforcement_learning/util.py
+187
-51
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CMakeLists.txt
..._code/gluon/reinforcementModel/mountaincar/CMakeLists.txt
+27
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNBufferFile.h
...code/gluon/reinforcementModel/mountaincar/CNNBufferFile.h
+51
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNCreator_mountaincar_master_actor.py
...tModel/mountaincar/CNNCreator_mountaincar_master_actor.py
+56
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNDataLoader_mountaincar_master_actor.py
...del/mountaincar/CNNDataLoader_mountaincar_master_actor.py
+57
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNNet_mountaincar_master_actor.py
...ementModel/mountaincar/CNNNet_mountaincar_master_actor.py
+105
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNPredictor_mountaincar_master_actor.h
...Model/mountaincar/CNNPredictor_mountaincar_master_actor.h
+104
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNTrainer_mountaincar_master_actor.py
...tModel/mountaincar/CNNTrainer_mountaincar_master_actor.py
+115
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNTranslator.h
...code/gluon/reinforcementModel/mountaincar/CNNTranslator.h
+127
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/HelperA.h
...arget_code/gluon/reinforcementModel/mountaincar/HelperA.h
+141
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/cmake/FindArmadillo.cmake
.../reinforcementModel/mountaincar/cmake/FindArmadillo.cmake
+38
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/mountaincar_master.cpp
...uon/reinforcementModel/mountaincar/mountaincar_master.cpp
+1
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/mountaincar_master.h
...gluon/reinforcementModel/mountaincar/mountaincar_master.h
+28
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/mountaincar_master_actor.h
...reinforcementModel/mountaincar/mountaincar_master_actor.h
+31
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/CNNCreator_MountaincarCritic.py
...ar/reinforcement_learning/CNNCreator_MountaincarCritic.py
+56
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/CNNNet_MountaincarCritic.py
...aincar/reinforcement_learning/CNNNet_MountaincarCritic.py
+115
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/__init__.py
...ementModel/mountaincar/reinforcement_learning/__init__.py
+0
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/agent.py
...orcementModel/mountaincar/reinforcement_learning/agent.py
+900
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/cnnarch_logger.py
...odel/mountaincar/reinforcement_learning/cnnarch_logger.py
+93
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/environment.py
...ntModel/mountaincar/reinforcement_learning/environment.py
+64
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/replay_memory.py
...Model/mountaincar/reinforcement_learning/replay_memory.py
+208
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/strategy.py
...ementModel/mountaincar/reinforcement_learning/strategy.py
+172
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/util.py
...forcementModel/mountaincar/reinforcement_learning/util.py
+276
-0
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/start_training.sh
...de/gluon/reinforcementModel/mountaincar/start_training.sh
+2
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNDataLoader_torcs_agent_torcsAgent_dqn.py
...ntModel/torcs/CNNDataLoader_torcs_agent_torcsAgent_dqn.py
+57
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
...ementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
+60
-50
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/_torcs_agent_dqn_reward_executor.so
...einforcement_learning/_torcs_agent_dqn_reward_executor.so
+0
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/action_policy.py
...cementModel/torcs/reinforcement_learning/action_policy.py
+0
-73
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/agent.py
.../reinforcementModel/torcs/reinforcement_learning/agent.py
+769
-375
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/cnnarch_logger.py
...ementModel/torcs/reinforcement_learning/cnnarch_logger.py
+93
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/environment.py
...orcementModel/torcs/reinforcement_learning/environment.py
+1
-1
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/replay_memory.py
...cementModel/torcs/reinforcement_learning/replay_memory.py
+95
-42
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/strategy.py
...inforcementModel/torcs/reinforcement_learning/strategy.py
+172
-0
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/util.py
...n/reinforcementModel/torcs/reinforcement_learning/util.py
+187
-51
src/test/resources/target_code/gluon/reinforcementModel/torcs/reward/pylib/__init__.py
...e/gluon/reinforcementModel/torcs/reward/pylib/__init__.py
+0
-0
No files found.
src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java
View file @
a51b42b6
...
...
@@ -214,11 +214,12 @@ public class GenerationTest extends AbstractSymtabTest {
"HelperA.h"
,
"start_training.sh"
,
"reinforcement_learning/__init__.py"
,
"reinforcement_learning/
action_polic
y.py"
,
"reinforcement_learning/
strateg
y.py"
,
"reinforcement_learning/agent.py"
,
"reinforcement_learning/environment.py"
,
"reinforcement_learning/replay_memory.py"
,
"reinforcement_learning/util.py"
"reinforcement_learning/util.py"
,
"reinforcement_learning/cnnarch_logger.py"
)
);
}
...
...
@@ -262,11 +263,12 @@ public class GenerationTest extends AbstractSymtabTest {
"reward/pylib/armanpy/armanpy_3d.i"
,
"reward/pylib/armanpy/numpy.i"
,
"reinforcement_learning/__init__.py"
,
"reinforcement_learning/
action_polic
y.py"
,
"reinforcement_learning/
strateg
y.py"
,
"reinforcement_learning/agent.py"
,
"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"
)
);
...
...
@@ -292,5 +294,33 @@ public class GenerationTest extends AbstractSymtabTest {
String
[]
args
=
{
"-m"
,
"src/test/resources/models/reinforcementModel"
,
"-r"
,
"mountaincar.Master"
,
"-b"
,
"GLUON"
,
"-f"
,
"n"
,
"-c"
,
"n"
};
EMADLGeneratorCli
.
main
(
args
);
assertEquals
(
0
,
Log
.
getFindings
().
stream
().
filter
(
Finding:
:
isError
).
count
());
checkFilesAreEqual
(
Paths
.
get
(
"./target/generated-sources-emadl"
),
Paths
.
get
(
"./src/test/resources/target_code/gluon/reinforcementModel/mountaincar"
),
Arrays
.
asList
(
"mountaincar_master.cpp"
,
"mountaincar_master.h"
,
"mountaincar_master_actor.h"
,
"CMakeLists.txt"
,
"CNNBufferFile.h"
,
"CNNCreator_mountaincar_master_actor.py"
,
"CNNNet_mountaincar_master_actor.py"
,
"CNNPredictor_mountaincar_master_actor.h"
,
"CNNTrainer_mountaincar_master_actor.py"
,
"CNNTranslator.h"
,
"HelperA.h"
,
"start_training.sh"
,
"reinforcement_learning/__init__.py"
,
"reinforcement_learning/CNNCreator_MountaincarCritic.py"
,
"reinforcement_learning/CNNNet_MountaincarCritic.py"
,
"reinforcement_learning/strategy.py"
,
"reinforcement_learning/agent.py"
,
"reinforcement_learning/environment.py"
,
"reinforcement_learning/replay_memory.py"
,
"reinforcement_learning/util.py"
,
"reinforcement_learning/cnnarch_logger.py"
)
);
}
}
src/test/resources/models/reinforcementModel/cartpole/agent/CartPoleDQN.cnnt
View file @
a51b42b6
...
...
@@ -24,7 +24,7 @@ configuration CartPoleDQN {
sample_size : 32
}
action_selection
: epsgreedy{
strategy
: epsgreedy{
epsilon : 1.0
min_epsilon : 0.01
epsilon_decay_method: linear
...
...
src/test/resources/models/reinforcementModel/mountaincar/agent/MountaincarActor.cnnt
View file @
a51b42b6
...
...
@@ -14,21 +14,26 @@ configuration MountaincarActor {
snapshot_interval : 20
loss : euclidean
replay_memory : buffer{
memory_size : 10000
sample_size :
32
memory_size : 10000
00
sample_size :
64
}
action_selection : epsgreedy
{
strategy : ornstein_uhlenbeck
{
epsilon : 1.0
min_epsilon : 0.01
epsilon_decay_method: linear
epsilon_decay : 0.01
mu: (0.0)
theta: (0.15)
sigma: (0.3)
}
actor_optimizer : adam {
learning_rate : 0.0001
}
optimizer : rmsprop
{
critic_optimizer : adam
{
learning_rate : 0.001
}
}
\ No newline at end of file
src/test/resources/models/reinforcementModel/mountaincar/agent/MountaincarCritic.cnna
View file @
a51b42b6
...
...
@@ -8,8 +8,5 @@ implementation Critic(state, action) {
FullyConnected(units=300)
) ->
Add() ->
Relu() ->
FullyConnected(units=1) ->
Tanh() ->
critic
Relu()
}
\ No newline at end of file
src/test/resources/models/reinforcementModel/torcs/agent/dqn/TorcsDQN.cnnt
View file @
a51b42b6
...
...
@@ -30,7 +30,7 @@ configuration TorcsDQN {
sample_size : 32
}
action_selection
: epsgreedy{
strategy
: epsgreedy{
epsilon : 1.0
min_epsilon : 0.01
epsilon_decay_method: linear
...
...
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNDataLoader_cartpole_master_dqn.py
0 → 100644
View file @
a51b42b6
import
os
import
h5py
import
mxnet
as
mx
import
logging
import
sys
class
cartpole_master_dqnDataLoader
:
_input_names_
=
[
'state'
]
_output_names_
=
[
'qvalues_label'
]
def
__init__
(
self
):
self
.
_data_dir
=
"data/"
def
load_data
(
self
,
batch_size
):
train_h5
,
test_h5
=
self
.
load_h5_files
()
data_mean
=
train_h5
[
self
.
_input_names_
[
0
]][:].
mean
(
axis
=
0
)
data_std
=
train_h5
[
self
.
_input_names_
[
0
]][:].
std
(
axis
=
0
)
+
1e-5
train_iter
=
mx
.
io
.
NDArrayIter
(
train_h5
[
self
.
_input_names_
[
0
]],
train_h5
[
self
.
_output_names_
[
0
]],
batch_size
=
batch_size
,
data_name
=
self
.
_input_names_
[
0
],
label_name
=
self
.
_output_names_
[
0
])
test_iter
=
None
if
test_h5
!=
None
:
test_iter
=
mx
.
io
.
NDArrayIter
(
test_h5
[
self
.
_input_names_
[
0
]],
test_h5
[
self
.
_output_names_
[
0
]],
batch_size
=
batch_size
,
data_name
=
self
.
_input_names_
[
0
],
label_name
=
self
.
_output_names_
[
0
])
return
train_iter
,
test_iter
,
data_mean
,
data_std
def
load_h5_files
(
self
):
train_h5
=
None
test_h5
=
None
train_path
=
self
.
_data_dir
+
"train.h5"
test_path
=
self
.
_data_dir
+
"test.h5"
if
os
.
path
.
isfile
(
train_path
):
train_h5
=
h5py
.
File
(
train_path
,
'r'
)
if
not
(
self
.
_input_names_
[
0
]
in
train_h5
and
self
.
_output_names_
[
0
]
in
train_h5
):
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
train_path
)
+
"' has to contain the datasets: "
+
"'"
+
self
.
_input_names_
[
0
]
+
"', '"
+
self
.
_output_names_
[
0
]
+
"'"
)
sys
.
exit
(
1
)
test_iter
=
None
if
os
.
path
.
isfile
(
test_path
):
test_h5
=
h5py
.
File
(
test_path
,
'r'
)
if
not
(
self
.
_input_names_
[
0
]
in
test_h5
and
self
.
_output_names_
[
0
]
in
test_h5
):
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
test_path
)
+
"' has to contain the datasets: "
+
"'"
+
self
.
_input_names_
[
0
]
+
"', '"
+
self
.
_output_names_
[
0
]
+
"'"
)
sys
.
exit
(
1
)
else
:
logging
.
warning
(
"Couldn't load test set. File '"
+
os
.
path
.
abspath
(
test_path
)
+
"' does not exist."
)
return
train_h5
,
test_h5
else
:
logging
.
error
(
"Data loading failure. File '"
+
os
.
path
.
abspath
(
train_path
)
+
"' does not exist."
)
sys
.
exit
(
1
)
\ No newline at end of file
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
View file @
a51b42b6
from
reinforcement_learning.agent
import
DqnAgent
from
reinforcement_learning.util
import
AgentSignalHandler
from
reinforcement_learning.cnnarch_logger
import
ArchLogger
import
reinforcement_learning.environment
import
CNNCreator_cartpole_master_dqn
...
...
@@ -9,9 +10,6 @@ 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
)
...
...
@@ -32,60 +30,73 @@ def resume_session():
break
return
resume_session
,
resume_directory
if
__name__
==
"__main__"
:
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
()))
ArchLogger
.
set_output_directory
(
output_directory
)
ArchLogger
.
set_logger_name
(
agent_name
)
ArchLogger
.
set_output_level
(
ArchLogger
.
INFO
)
env
=
reinforcement_learning
.
environment
.
GymEnvironment
(
'CartPole-v0'
)
context
=
mx
.
cpu
()
net_creator
=
CNNCreator_cartpole_master_dqn
.
CNNCreator_cartpole_master_dqn
()
net_creator
.
construct
(
context
)
replay_memory_params
=
{
'method'
:
'buffer'
,
'memory_size'
:
10000
,
'sample_size'
:
32
,
'state_dtype'
:
'float32'
,
'action_dtype'
:
'uint8'
,
'rewards_dtype'
:
'float32'
}
context
=
mx
.
cpu
()
qnet_creator
=
CNNCreator_cartpole_master_dqn
.
CNNCreator_cartpole_master_dqn
()
qnet_creator
.
construct
(
context
)
policy_params
=
{
'method'
:
'epsgreedy'
,
'epsilon'
:
1
,
'min_epsilon'
:
0.01
,
'epsilon_decay_method'
:
'linear'
,
'epsilon_decay'
:
0.01
,
agent_params
=
{
'environment'
:
env
,
'replay_memory_params'
:
{
'method'
:
'buffer'
,
'memory_size'
:
10000
,
'sample_size'
:
32
,
'state_dtype'
:
'float32'
,
'action_dtype'
:
'float32'
,
'rewards_dtype'
:
'float32'
},
'strategy_params'
:
{
'method'
:
'epsgreedy'
,
'epsilon'
:
1
,
'min_epsilon'
:
0.01
,
'epsilon_decay_method'
:
'linear'
,
'epsilon_decay'
:
0.01
,
},
'agent_name'
:
agent_name
,
'verbose'
:
True
,
'state_dim'
:
(
4
,),
'action_dim'
:
(
2
,),
'ctx'
:
'cpu'
,
'discount_factor'
:
0.999
,
'training_episodes'
:
160
,
'train_interval'
:
1
,
'snapshot_interval'
:
20
,
'max_episode_step'
:
250
,
'target_score'
:
185.5
,
'qnet'
:
qnet_creator
.
net
,
'use_fix_target'
:
True
,
'target_update_interval'
:
200
,
'loss_function'
:
'euclidean'
,
'optimizer'
:
'rmsprop'
,
'optimizer_params'
:
{
'learning_rate'
:
0.001
},
'double_dqn'
:
False
,
}
resume
_session
,
resume_directory
=
resume_session
()
resume
,
resume_directory
=
resume_session
()
if
resume_session
:
agent
=
DqnAgent
.
resume_from_session
(
resume_directory
,
net_creator
.
net
,
env
)
if
resume
:
resume_agent_params
=
{
'session_dir'
:
resume_directory
,
'environment'
:
env
,
'net'
:
qnet_creator
.
net
,
}
agent
=
DqnAgent
.
resume_from_session
(
**
resume_agent_params
)
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
)
agent
=
DqnAgent
(
**
agent_params
)
signal_handler
=
AgentSignalHandler
()
signal_handler
.
register_agent
(
agent
)
...
...
@@ -93,4 +104,4 @@ if __name__ == "__main__":
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
agent
.
save_best_network
(
qnet_creator
.
_model_dir_
+
qnet_creator
.
_model_prefix_
+
'_newest'
,
epoch
=
0
)
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/action_policy.py
deleted
100644 → 0
View file @
cc31bd8e
import
numpy
as
np
class
ActionPolicyBuilder
(
object
):
def
__init__
(
self
):
pass
def
build_by_params
(
self
,
method
=
'epsgreedy'
,
epsilon
=
0.5
,
min_epsilon
=
0.05
,
epsilon_decay_method
=
'no'
,
epsilon_decay
=
0.0
,
action_dim
=
None
):
if
epsilon_decay_method
==
'linear'
:
decay
=
LinearDecay
(
eps_decay
=
epsilon_decay
,
min_eps
=
min_epsilon
)
else
:
decay
=
NoDecay
()
if
method
==
'epsgreedy'
:
assert
action_dim
is
not
None
assert
len
(
action_dim
)
==
1
return
EpsilonGreedyActionPolicy
(
eps
=
epsilon
,
number_of_actions
=
action_dim
[
0
],
decay
=
decay
)
else
:
assert
action_dim
is
not
None
assert
len
(
action_dim
)
==
1
return
GreedyActionPolicy
()
class
EpsilonGreedyActionPolicy
(
object
):
def
__init__
(
self
,
eps
,
number_of_actions
,
decay
):
self
.
eps
=
eps
self
.
cur_eps
=
eps
self
.
__number_of_actions
=
number_of_actions
self
.
__decay_method
=
decay
def
select_action
(
self
,
values
):
do_exploration
=
(
np
.
random
.
rand
()
<
self
.
cur_eps
)
if
do_exploration
:
action
=
np
.
random
.
randint
(
low
=
0
,
high
=
self
.
__number_of_actions
)
else
:
action
=
values
.
asnumpy
().
argmax
()
return
action
def
decay
(
self
):
self
.
cur_eps
=
self
.
__decay_method
.
decay
(
self
.
cur_eps
)
class
GreedyActionPolicy
(
object
):
def
__init__
(
self
):
pass
def
select_action
(
self
,
values
):
return
values
.
asnumpy
().
argmax
()
def
decay
(
self
):
pass
class
NoDecay
(
object
):
def
__init__
(
self
):
pass
def
decay
(
self
,
cur_eps
):
return
cur_eps
class
LinearDecay
(
object
):
def
__init__
(
self
,
eps_decay
,
min_eps
=
0
):
self
.
eps_decay
=
eps_decay
self
.
min_eps
=
min_eps
def
decay
(
self
,
cur_eps
):
return
max
(
cur_eps
-
self
.
eps_decay
,
self
.
min_eps
)
\ No newline at end of file
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/agent.py
View file @
a51b42b6
This diff is collapsed.
Click to expand it.
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/cnnarch_logger.py
0 → 100644
View file @
a51b42b6
import
logging
import
sys
import
os
import
util
class
ArchLogger
(
object
):
_logger
=
None
__output_level
=
logging
.
INFO
__logger_name
=
'agent'
__output_directory
=
'.'
__append
=
True
__logformat
=
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
__dateformat
=
'%d-%b-%y %H:%M:%S'
INFO
=
logging
.
INFO
DEBUG
=
logging
.
DEBUG
@
staticmethod
def
set_output_level
(
output_level
):
assert
output_level
is
not
None
ArchLogger
.
__output_level
=
output_level
@
staticmethod
def
set_logger_name
(
logger_name
):
assert
logger_name
is
not
None
ArchLogger
.
__logger_name
=
logger_name
@
staticmethod
def
set_output_directory
(
output_directory
):
assert
output_directory
is
not
None
ArchLogger
.
__output_directory
=
output_directory
@
staticmethod
def
set_append
(
append
):
assert
append
is
not
None
ArchLogger
.
__append
=
append
@
staticmethod
def
set_log_format
(
logformat
,
dateformat
):
assert
logformat
is
not
None
assert
dateformat
is
not
None
ArchLogger
.
__logformat
=
logformat
ArchLogger
.
__dateformat
=
dateformat
@
staticmethod
def
init_logger
(
make_log_file
=
True
):
assert
ArchLogger
.
_logger
is
None
,
'Logger init already called'
filemode
=
'a'
if
ArchLogger
.
__append
else
'w'
formatter
=
logging
.
Formatter
(
fmt
=
ArchLogger
.
__logformat
,
datefmt
=
ArchLogger
.
__dateformat
)
logger
=
logging
.
getLogger
(
ArchLogger
.
__logger_name
)
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
)
ArchLogger
.
_logger
=
logger
@
staticmethod
def
get_logger
():
if
ArchLogger
.
_logger
is
None
:
ArchLogger
.
init_logger
()
assert
ArchLogger
.
_logger
is
not
None
return
ArchLogger
.
_logger
if
__name__
==
"__main__"
:
print
(
'=== Test logger ==='
)
ArchLogger
.
set_logger_name
(
'TestLogger'
)
ArchLogger
.
set_output_directory
(
'test_log'
)
ArchLogger
.
init_logger
()
logger
=
ArchLogger
.
get_logger
()
logger
.
warning
(
'This is a warning'
)
logger
.
debug
(
'This is a debug information, which you should not see'
)
logger
.
info
(
'This is a normal information'
)
assert
os
.
path
.
exists
(
'test_log'
)
\
and
os
.
path
.
isfile
(
os
.
path
.
join
(
'test_log'
,
'TestLogger.log'
)),
\
'Test failed: No logfile exists'
import
shutil
shutil
.
rmtree
(
'test_log'
)
\ No newline at end of file
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/environment.py
View file @
a51b42b6
...
...
@@ -33,13 +33,6 @@ class GymEnvironment(Environment):
def
state_dim
(
self
):
return
self
.
__env
.
observation_space
.
shape
@
property
def
state_dtype
(
self
):
return
'float32'
@
property
def
action_dtype
(
self
):
return
'uint8'
@
property
def
number_of_actions
(
self
):
...
...
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/replay_memory.py
View file @
a51b42b6
import
numpy
as
np
class
ReplayMemoryBuilder
(
object
):
def
__init__
(
self
):
self
.
__supported_methods
=
[
'online'
,
'buffer'
,
'combined'
]
def
build_by_params
(
self
,
def
build_by_params
(
self
,
state_dim
,
method
=
'online'
,
state_dtype
=
'float32'
,
action_dim
=
(
1
,),
action_dtype
=
'uint8'
,
rewards_dtype
=
'float32'
,
memory_size
=
1000
,
sample_size
=
32
):
sample_size
=
32
):
assert
state_dim
is
not
None
assert
action_dim
is
not
None
assert
method
in
self
.
__supported_methods
if
method
==
'online'
:
return
self
.
build_online_memory
(
state_dim
=
state_dim
,
state_dtype
=
state_dtype
,
action_dtype
=
action_dtype
,
rewards_dtype
=
rewards_dtype
)
return
self
.
build_online_memory
(
state_dim
=
state_dim
,
state_dtype
=
state_dtype
,
action_dtype
=
action_dtype
,
action_dim
=
action_dim
,
rewards_dtype
=
rewards_dtype
)
else
:
assert
memory_size
is
not
None
and
memory_size
>
0
assert
sample_size
is
not
None
and
sample_size
>
0
if
method
==
'buffer'
:
return
self
.
build_buffered_memory
(
state_dim
=
state_dim
,
sample_size
=
sample_size
,
memory_size
=
memory_size
,
state_dtype
=
state_dtype
,
action_dtype
=
action_dtype
,
return
self
.
build_buffered_memory
(
state_dim
=
state_dim
,
sample_size
=
sample_size
,
memory_size
=
memory_size
,
state_dtype
=
state_dtype
,
action_dim
=
action_dim
,
action_dtype
=
action_dtype
,
rewards_dtype
=
rewards_dtype
)
else
:
return
self
.
build_combined_memory
(
state_dim
=
state_dim
,
sample_size
=
sample_size
,
memory_size
=
memory_size
,
state_dtype
=
state_dtype
,
action_dtype
=
action_dtype
,
return
self
.
build_combined_memory
(
state_dim
=
state_dim
,
sample_size
=
sample_size
,
memory_size
=
memory_size
,
state_dtype
=
state_dtype
,