Skip to content
GitLab
Projects
Groups
Snippets
Help
Loading...
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Sign in
Toggle navigation
E
EMADL2CPP
Project overview
Project overview
Details
Activity
Releases
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Locked Files
Issues
10
Issues
10
List
Boards
Labels
Service Desk
Milestones
Iterations
Merge Requests
0
Merge Requests
0
Requirements
Requirements
List
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Test Cases
Security & Compliance
Security & Compliance
Dependency List
License Compliance
Operations
Operations
Incidents
Environments
Packages & Registries
Packages & Registries
Container Registry
Analytics
Analytics
CI / CD
Code Review
Insights
Issue
Repository
Value Stream
Wiki
Wiki
Snippets
Snippets
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
a51b42b6
Commit
a51b42b6
authored
May 30, 2019
by
Nicola Gatto
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Adjust tests
parent
cc31bd8e
Changes
48
Hide whitespace changes
Inline
Side-by-side
Showing
48 changed files
with
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
...
...
@@ -2,118 +2,382 @@ import mxnet as mx
import
numpy
as
np
import
time
import
os
import
logging
import
sys
import
util
import
matplotlib.pyplot
as
plt
import
pyprind
from
cnnarch_logger
import
ArchLogger
from
replay_memory
import
ReplayMemoryBuilder
from
action_policy
import
ActionPolicyBuilder
from
util
import
copy_net
,
get_loss_function
from
strategy
import
StrategyBuilder
from
util
import
copy_net
,
get_loss_function
,
\
copy_net_with_two_inputs
,
DdpgTrainingStats
,
DqnTrainingStats
,
\
make_directory_if_not_exist
from
mxnet
import
nd
,
gluon
,
autograd
class
DqnAgent
(
object
):
def
__init__
(
self
,
network
,
class
Agent
(
object
):
def
__init__
(
self
,
environment
,
replay_memory_params
,
polic
y_params
,
strateg
y_params
,
state_dim
,
action_dim
,
ctx
=
None
,
discount_factor
=
.
9
,
loss_function
=
'euclidean'
,
optimizer
=
'rmsprop'
,
optimizer_params
=
{
'learning_rate'
:
0.09
},
training_episodes
=
50
,
train_interval
=
1
,
use_fix_target
=
False
,
double_dqn
=
False
,
target_update_interval
=
10
,
start_training
=
0
,
snapshot_interval
=
200
,
agent_name
=
'
Dqn_a
gent'
,
agent_name
=
'
A
gent'
,
max_episode_step
=
99999
,
evaluation_samples
=
1000
,
output_directory
=
'model_parameters'
,
verbose
=
True
,
live_plot
=
True
,
make_logfile
=
True
,
target_score
=
None
):
assert
0
<
discount_factor
<=
1
assert
train_interval
>
0
assert
target_update_interval
>
0
assert
snapshot_interval
>
0
assert
max_episode_step
>
0
assert
training_episodes
>
0
assert
replay_memory_params
is
not
None
assert
type
(
state_dim
)
is
tuple
self
.
__ctx
=
mx
.
gpu
()
if
ctx
==
'gpu'
else
mx
.
cpu
()
self
.
__qnet
=
network
self
.
_
_environment
=
environment
self
.
_
_discount_factor
=
discount_factor
self
.
_
_training_episodes
=
training_episodes
self
.
_
_train_interval
=
train_interval
self
.
_
_verbose
=
verbose
self
.
_
_state_dim
=
state_dim
self
.
_
_action_dim
=
self
.
__qnet
(
nd
.
random_normal
(
shape
=
((
1
,)
+
self
.
__state_dim
),
ctx
=
self
.
__ctx
)).
shape
[
1
:]
target_score
=
None
):
assert
0
<
discount_factor
<=
1
,
\
'Discount factor must be between 0 and 1'
assert
train_interval
>
0
,
'Train interval must be greater 0'
assert
snapshot_interval
>
0
,
'Snapshot interval must be greater 0'
assert
max_episode_step
>
0
,
\
'Maximal steps per episode must be greater 0'
assert
training_episodes
>
0
,
'Trainings episode must be greater 0'
assert
replay_memory_params
is
not
None
,
\
'Replay memory parameter not set'
assert
type
(
state_dim
)
is
tuple
,
'State dimension is not a tuple'
assert
type
(
action_dim
)
is
tuple
,
'Action dimension is not a tuple'
self
.
_logger
=
ArchLogger
.
get_logger
()
self
.
_
ctx
=
mx
.
gpu
()
if
ctx
==
'gpu'
else
mx
.
cpu
()
self
.
_
environment
=
environment
self
.
_
discount_factor
=
discount_factor
self
.
_
training_episodes
=
training_episodes
self
.
_
train_interval
=
train_interval
self
.
_
verbose
=
verbose
self
.
_
state_dim
=
state_dim
replay_memory_params
[
'state_dim'
]
=
state_dim
self
.
__replay_memory_params
=
replay_memory_params
replay_memory_params
[
'action_dim'
]
=
action_dim
self
.
_replay_memory_params
=
replay_memory_params
rm_builder
=
ReplayMemoryBuilder
()
self
.
__memory
=
rm_builder
.
build_by_params
(
**
replay_memory_params
)
self
.
__minibatch_size
=
self
.
__memory
.
sample_size
policy_params
[
'action_dim'
]
=
self
.
__action_dim
self
.
__policy_params
=
policy_params
p_builder
=
ActionPolicyBuilder
()
self
.
__policy
=
p_builder
.
build_by_params
(
**
policy_params
)
self
.
__target_update_interval
=
target_update_interval
self
.
__target_qnet
=
copy_net
(
self
.
__qnet
,
self
.
__state_dim
,
ctx
=
self
.
__ctx
)
self
.
__loss_function_str
=
loss_function
self
.
__loss_function
=
get_loss_function
(
loss_function
)
self
.
__agent_name
=
agent_name
self
.
__snapshot_interval
=
snapshot_interval
self
.
__creation_time
=
time
.
time
()
self
.
__max_episode_step
=
max_episode_step
self
.
__optimizer
=
optimizer
self
.
__optimizer_params
=
optimizer_params
self
.
__make_logfile
=
make_logfile
self
.
__double_dqn
=
double_dqn
self
.
__use_fix_target
=
use_fix_target
self
.
__live_plot
=
live_plot
self
.
__user_given_directory
=
output_directory
self
.
__target_score
=
target_score
self
.
__interrupt_flag
=
False
self
.
_memory
=
rm_builder
.
build_by_params
(
**
replay_memory_params
)
self
.
_minibatch_size
=
self
.
_memory
.
sample_size
self
.
_action_dim
=
action_dim
strategy_params
[
'action_dim'
]
=
self
.
_action_dim
self
.
_strategy_params
=
strategy_params
strategy_builder
=
StrategyBuilder
()
self
.
_strategy
=
strategy_builder
.
build_by_params
(
**
strategy_params
)
self
.
_agent_name
=
agent_name
self
.
_snapshot_interval
=
snapshot_interval
self
.
_creation_time
=
time
.
time
()