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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
869ec892
Commit
869ec892
authored
Jul 17, 2019
by
Nicola Gatto
Browse files
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Browse Files
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Adapt tests to new templates
parent
1fb97868
Changes
13
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13 changed files
with
1332 additions
and
139 deletions
+1332
-139
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
...forcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
+3
-3
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/agent.py
...inforcementModel/cartpole/reinforcement_learning/agent.py
+385
-33
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/strategy.py
...orcementModel/cartpole/reinforcement_learning/strategy.py
+52
-6
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/util.py
...einforcementModel/cartpole/reinforcement_learning/util.py
+2
-2
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNTrainer_mountaincar_master_actor.py
...tModel/mountaincar/CNNTrainer_mountaincar_master_actor.py
+3
-3
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/agent.py
...orcementModel/mountaincar/reinforcement_learning/agent.py
+385
-33
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/strategy.py
...ementModel/mountaincar/reinforcement_learning/strategy.py
+52
-6
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/util.py
...forcementModel/mountaincar/reinforcement_learning/util.py
+2
-2
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
...ementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
+3
-3
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/agent.py
.../reinforcementModel/torcs/reinforcement_learning/agent.py
+385
-33
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/environment.py
...orcementModel/torcs/reinforcement_learning/environment.py
+6
-7
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/strategy.py
...inforcementModel/torcs/reinforcement_learning/strategy.py
+52
-6
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/util.py
...n/reinforcementModel/torcs/reinforcement_learning/util.py
+2
-2
No files found.
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNTrainer_cartpole_master_dqn.py
View file @
869ec892
...
...
@@ -78,10 +78,10 @@ if __name__ == "__main__":
'snapshot_interval'
:
20
,
'max_episode_step'
:
250
,
'target_score'
:
185.5
,
'qnet'
:
qnet_creator
.
net
,
'qnet'
:
qnet_creator
.
net
works
[
0
]
,
'use_fix_target'
:
True
,
'target_update_interval'
:
200
,
'loss'
:
'huber'
,
'loss
_function
'
:
'huber'
,
'optimizer'
:
'rmsprop'
,
'optimizer_params'
:
{
'learning_rate'
:
0.001
},
...
...
@@ -108,4 +108,4 @@ if __name__ == "__main__":
train_successful
=
agent
.
train
()
if
train_successful
:
agent
.
save_best_network
(
qnet_creator
.
_model_dir_
+
qnet_creator
.
_model_prefix_
+
'_0_newest'
,
epoch
=
0
)
agent
.
export_best_network
(
path
=
qnet_creator
.
_model_dir_
+
qnet_creator
.
_model_prefix_
+
'_0_newest'
,
epoch
=
0
)
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/agent.py
View file @
869ec892
This diff is collapsed.
Click to expand it.
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/strategy.py
View file @
869ec892
...
...
@@ -13,18 +13,21 @@ class StrategyBuilder(object):
epsilon_decay_method
=
'no'
,
epsilon_decay
=
0.0
,
epsilon_decay_start
=
0
,
epsilon_decay_per_step
=
False
,
action_dim
=
None
,
action_low
=
None
,
action_high
=
None
,
mu
=
0.0
,
theta
=
0.5
,
sigma
=
0.3
sigma
=
0.3
,
noise_variance
=
0.1
):
if
epsilon_decay_method
==
'linear'
:
decay
=
LinearDecay
(
eps_decay
=
epsilon_decay
,
min_eps
=
min_epsilon
,
decay_start
=
epsilon_decay_start
)
decay_start
=
epsilon_decay_start
,
decay_per_step
=
epsilon_decay_per_step
)
else
:
decay
=
NoDecay
()
...
...
@@ -44,6 +47,13 @@ class StrategyBuilder(object):
return
OrnsteinUhlenbeckStrategy
(
action_dim
,
action_low
,
action_high
,
epsilon
,
mu
,
theta
,
sigma
,
decay
)
elif
method
==
'gaussian'
:
assert
action_dim
is
not
None
assert
action_low
is
not
None
assert
action_high
is
not
None
assert
noise_variance
is
not
None
return
GaussianNoiseStrategy
(
action_dim
,
action_low
,
action_high
,
epsilon
,
noise_variance
,
decay
)
else
:
assert
action_dim
is
not
None
assert
len
(
action_dim
)
==
1
...
...
@@ -70,17 +80,27 @@ class NoDecay(BaseDecay):
class
LinearDecay
(
BaseDecay
):
def
__init__
(
self
,
eps_decay
,
min_eps
=
0
,
decay_start
=
0
):
def
__init__
(
self
,
eps_decay
,
min_eps
=
0
,
decay_start
=
0
,
decay_per_step
=
False
):
super
(
LinearDecay
,
self
).
__init__
()
self
.
eps_decay
=
eps_decay
self
.
min_eps
=
min_eps
self
.
decay_start
=
decay_start
self
.
decay_per_step
=
decay_per_step
self
.
last_episode
=
-
1
def
d
ecay
(
self
,
cur_eps
,
episode
):
if
episode
<
self
.
decay_start
:
return
cur_eps
def
d
o_decay
(
self
,
episode
):
if
self
.
decay_per_step
:
do
=
(
episode
>=
self
.
decay_start
)
else
:
do
=
((
self
.
last_episode
!=
episode
)
and
(
episode
>=
self
.
decay_start
))
self
.
last_episode
=
episode
return
do
def
decay
(
self
,
cur_eps
,
episode
):
if
self
.
do_decay
(
episode
):
return
max
(
cur_eps
-
self
.
eps_decay
,
self
.
min_eps
)
else
:
return
cur_eps
class
BaseStrategy
(
object
):
...
...
@@ -170,3 +190,29 @@ class OrnsteinUhlenbeckStrategy(BaseStrategy):
noise
=
self
.
_evolve_state
()
action
=
(
1.0
-
self
.
cur_eps
)
*
values
+
(
self
.
cur_eps
*
noise
)
return
np
.
clip
(
action
,
self
.
_action_low
,
self
.
_action_high
)
class
GaussianNoiseStrategy
(
BaseStrategy
):
def
__init__
(
self
,
action_dim
,
action_low
,
action_high
,
eps
,
noise_variance
,
decay
=
NoDecay
()
):
super
(
GaussianNoiseStrategy
,
self
).
__init__
(
decay
)
self
.
eps
=
eps
self
.
cur_eps
=
eps
self
.
_action_dim
=
action_dim
self
.
_action_low
=
action_low
self
.
_action_high
=
action_high
self
.
_noise_variance
=
noise_variance
def
select_action
(
self
,
values
):
noise
=
np
.
random
.
normal
(
loc
=
0.0
,
scale
=
self
.
_noise_variance
,
size
=
self
.
_action_dim
)
action
=
values
+
self
.
cur_eps
*
noise
return
np
.
clip
(
action
,
self
.
_action_low
,
self
.
_action_high
)
src/test/resources/target_code/gluon/reinforcementModel/cartpole/reinforcement_learning/util.py
View file @
869ec892
...
...
@@ -11,8 +11,8 @@ import cnnarch_logger
LOSS_FUNCTIONS
=
{
'l1'
:
gluon
.
loss
.
L1Loss
(),
'
euclidean
'
:
gluon
.
loss
.
L2Loss
(),
'huber
_loss
'
:
gluon
.
loss
.
HuberLoss
(),
'
l2
'
:
gluon
.
loss
.
L2Loss
(),
'huber'
:
gluon
.
loss
.
HuberLoss
(),
'softmax_cross_entropy'
:
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(),
'sigmoid_cross_entropy'
:
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()}
...
...
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNTrainer_mountaincar_master_actor.py
View file @
869ec892
...
...
@@ -85,8 +85,8 @@ if __name__ == "__main__":
'train_interval'
:
1
,
'snapshot_interval'
:
20
,
'max_episode_step'
:
1000
,
'actor'
:
actor_creator
.
net
,
'critic'
:
critic_creator
.
net
,
'actor'
:
actor_creator
.
net
works
[
0
]
,
'critic'
:
critic_creator
.
net
works
[
0
]
,
'actor_optimizer'
:
'adam'
,
'actor_optimizer_params'
:
{
'learning_rate'
:
1.0E-4
},
...
...
@@ -116,4 +116,4 @@ if __name__ == "__main__":
train_successful
=
agent
.
train
()
if
train_successful
:
agent
.
save_best_network
(
actor_creator
.
_model_dir_
+
actor_creator
.
_model_prefix_
+
'_0_newest'
,
epoch
=
0
)
agent
.
export_best_network
(
path
=
actor_creator
.
_model_dir_
+
actor_creator
.
_model_prefix_
+
'_0_newest'
,
epoch
=
0
)
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/agent.py
View file @
869ec892
This diff is collapsed.
Click to expand it.
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/strategy.py
View file @
869ec892
...
...
@@ -13,18 +13,21 @@ class StrategyBuilder(object):
epsilon_decay_method
=
'no'
,
epsilon_decay
=
0.0
,
epsilon_decay_start
=
0
,
epsilon_decay_per_step
=
False
,
action_dim
=
None
,
action_low
=
None
,
action_high
=
None
,
mu
=
0.0
,
theta
=
0.5
,
sigma
=
0.3
sigma
=
0.3
,
noise_variance
=
0.1
):
if
epsilon_decay_method
==
'linear'
:
decay
=
LinearDecay
(
eps_decay
=
epsilon_decay
,
min_eps
=
min_epsilon
,
decay_start
=
epsilon_decay_start
)
decay_start
=
epsilon_decay_start
,
decay_per_step
=
epsilon_decay_per_step
)
else
:
decay
=
NoDecay
()
...
...
@@ -44,6 +47,13 @@ class StrategyBuilder(object):
return
OrnsteinUhlenbeckStrategy
(
action_dim
,
action_low
,
action_high
,
epsilon
,
mu
,
theta
,
sigma
,
decay
)
elif
method
==
'gaussian'
:
assert
action_dim
is
not
None
assert
action_low
is
not
None
assert
action_high
is
not
None
assert
noise_variance
is
not
None
return
GaussianNoiseStrategy
(
action_dim
,
action_low
,
action_high
,
epsilon
,
noise_variance
,
decay
)
else
:
assert
action_dim
is
not
None
assert
len
(
action_dim
)
==
1
...
...
@@ -70,17 +80,27 @@ class NoDecay(BaseDecay):
class
LinearDecay
(
BaseDecay
):
def
__init__
(
self
,
eps_decay
,
min_eps
=
0
,
decay_start
=
0
):
def
__init__
(
self
,
eps_decay
,
min_eps
=
0
,
decay_start
=
0
,
decay_per_step
=
False
):
super
(
LinearDecay
,
self
).
__init__
()
self
.
eps_decay
=
eps_decay
self
.
min_eps
=
min_eps
self
.
decay_start
=
decay_start
self
.
decay_per_step
=
decay_per_step
self
.
last_episode
=
-
1
def
d
ecay
(
self
,
cur_eps
,
episode
):
if
episode
<
self
.
decay_start
:
return
cur_eps
def
d
o_decay
(
self
,
episode
):
if
self
.
decay_per_step
:
do
=
(
episode
>=
self
.
decay_start
)
else
:
do
=
((
self
.
last_episode
!=
episode
)
and
(
episode
>=
self
.
decay_start
))
self
.
last_episode
=
episode
return
do
def
decay
(
self
,
cur_eps
,
episode
):
if
self
.
do_decay
(
episode
):
return
max
(
cur_eps
-
self
.
eps_decay
,
self
.
min_eps
)
else
:
return
cur_eps
class
BaseStrategy
(
object
):
...
...
@@ -170,3 +190,29 @@ class OrnsteinUhlenbeckStrategy(BaseStrategy):
noise
=
self
.
_evolve_state
()
action
=
(
1.0
-
self
.
cur_eps
)
*
values
+
(
self
.
cur_eps
*
noise
)
return
np
.
clip
(
action
,
self
.
_action_low
,
self
.
_action_high
)
class
GaussianNoiseStrategy
(
BaseStrategy
):
def
__init__
(
self
,
action_dim
,
action_low
,
action_high
,
eps
,
noise_variance
,
decay
=
NoDecay
()
):
super
(
GaussianNoiseStrategy
,
self
).
__init__
(
decay
)
self
.
eps
=
eps
self
.
cur_eps
=
eps
self
.
_action_dim
=
action_dim
self
.
_action_low
=
action_low
self
.
_action_high
=
action_high
self
.
_noise_variance
=
noise_variance
def
select_action
(
self
,
values
):
noise
=
np
.
random
.
normal
(
loc
=
0.0
,
scale
=
self
.
_noise_variance
,
size
=
self
.
_action_dim
)
action
=
values
+
self
.
cur_eps
*
noise
return
np
.
clip
(
action
,
self
.
_action_low
,
self
.
_action_high
)
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/util.py
View file @
869ec892
...
...
@@ -11,8 +11,8 @@ import cnnarch_logger
LOSS_FUNCTIONS
=
{
'l1'
:
gluon
.
loss
.
L1Loss
(),
'
euclidean
'
:
gluon
.
loss
.
L2Loss
(),
'huber
_loss
'
:
gluon
.
loss
.
HuberLoss
(),
'
l2
'
:
gluon
.
loss
.
L2Loss
(),
'huber'
:
gluon
.
loss
.
HuberLoss
(),
'softmax_cross_entropy'
:
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(),
'sigmoid_cross_entropy'
:
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()}
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNTrainer_torcs_agent_torcsAgent_dqn.py
View file @
869ec892
...
...
@@ -84,10 +84,10 @@ if __name__ == "__main__":
'train_interval'
:
1
,
'snapshot_interval'
:
1000
,
'max_episode_step'
:
999999999
,
'qnet'
:
qnet_creator
.
net
,
'qnet'
:
qnet_creator
.
net
works
[
0
]
,
'use_fix_target'
:
True
,
'target_update_interval'
:
500
,
'loss'
:
'huber'
,
'loss
_function
'
:
'huber'
,
'optimizer'
:
'rmsprop'
,
'optimizer_params'
:
{
'learning_rate'
:
0.001
},
...
...
@@ -114,4 +114,4 @@ if __name__ == "__main__":
train_successful
=
agent
.
train
()
if
train_successful
:
agent
.
save_best_network
(
qnet_creator
.
_model_dir_
+
qnet_creator
.
_model_prefix_
+
'_0_newest'
,
epoch
=
0
)
agent
.
export_best_network
(
path
=
qnet_creator
.
_model_dir_
+
qnet_creator
.
_model_prefix_
+
'_0_newest'
,
epoch
=
0
)
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/agent.py
View file @
869ec892
This diff is collapsed.
Click to expand it.
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/environment.py
View file @
869ec892
...
...
@@ -84,7 +84,6 @@ class RosEnvironment(Environment):
def
reset
(
self
):
self
.
__in_reset
=
True
time
.
sleep
(
0.5
)
reset_message
=
Bool
()
reset_message
.
data
=
True
self
.
__waiting_for_state_update
=
True
...
...
@@ -110,7 +109,8 @@ class RosEnvironment(Environment):
next_state
=
self
.
__last_received_state
terminal
=
self
.
__last_received_terminal
reward
=
self
.
__calc_reward
(
next_state
,
terminal
)
rospy
.
logdebug
(
'Calculated reward: {}'
.
format
(
reward
))
logger
.
debug
(
'Transition: ({}, {}, {}, {})'
.
format
(
action
,
reward
,
next_state
,
terminal
))
return
next_state
,
reward
,
terminal
,
0
...
...
@@ -129,20 +129,19 @@ class RosEnvironment(Environment):
else
:
rospy
.
logerr
(
"Timeout 3 times in a row: Terminate application"
)
exit
()
time
.
sleep
(
1
00
/
10
00
)
time
.
sleep
(
1
/
5
00
)
def
close
(
self
):
rospy
.
signal_shutdown
(
'Program ended!'
)
def
__state_callback
(
self
,
data
):
self
.
__last_received_state
=
np
.
array
(
data
.
data
,
dtype
=
'float32'
).
reshape
((
5
,))
rospy
.
log
debug
(
'Received state: {}'
.
format
(
self
.
__last_received_state
))
logger
.
debug
(
'Received state: {}'
.
format
(
self
.
__last_received_state
))
self
.
__waiting_for_state_update
=
False
def
__terminal_state_callback
(
self
,
data
):
self
.
__last_received_terminal
=
data
.
data
rospy
.
logdebug
(
'Received terminal flag: {}'
.
format
(
self
.
__last_received_terminal
))
logger
.
debug
(
'Received terminal: {}'
.
format
(
self
.
__last_received_terminal
))
self
.
__last_received_terminal
=
np
.
bool
(
data
.
data
)
logger
.
debug
(
'Received terminal flag: {}'
.
format
(
self
.
__last_received_terminal
))
self
.
__waiting_for_terminal_update
=
False
def
__calc_reward
(
self
,
state
,
terminal
):
...
...
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/strategy.py
View file @
869ec892
...
...
@@ -13,18 +13,21 @@ class StrategyBuilder(object):
epsilon_decay_method
=
'no'
,
epsilon_decay
=
0.0
,
epsilon_decay_start
=
0
,
epsilon_decay_per_step
=
False
,
action_dim
=
None
,
action_low
=
None
,
action_high
=
None
,
mu
=
0.0
,
theta
=
0.5
,
sigma
=
0.3
sigma
=
0.3
,
noise_variance
=
0.1
):
if
epsilon_decay_method
==
'linear'
:
decay
=
LinearDecay
(
eps_decay
=
epsilon_decay
,
min_eps
=
min_epsilon
,
decay_start
=
epsilon_decay_start
)
decay_start
=
epsilon_decay_start
,
decay_per_step
=
epsilon_decay_per_step
)
else
:
decay
=
NoDecay
()
...
...
@@ -44,6 +47,13 @@ class StrategyBuilder(object):
return
OrnsteinUhlenbeckStrategy
(
action_dim
,
action_low
,
action_high
,
epsilon
,
mu
,
theta
,
sigma
,
decay
)
elif
method
==
'gaussian'
:
assert
action_dim
is
not
None
assert
action_low
is
not
None
assert
action_high
is
not
None
assert
noise_variance
is
not
None
return
GaussianNoiseStrategy
(
action_dim
,
action_low
,
action_high
,
epsilon
,
noise_variance
,
decay
)
else
:
assert
action_dim
is
not
None
assert
len
(
action_dim
)
==
1
...
...
@@ -70,17 +80,27 @@ class NoDecay(BaseDecay):
class
LinearDecay
(
BaseDecay
):
def
__init__
(
self
,
eps_decay
,
min_eps
=
0
,
decay_start
=
0
):
def
__init__
(
self
,
eps_decay
,
min_eps
=
0
,
decay_start
=
0
,
decay_per_step
=
False
):
super
(
LinearDecay
,
self
).
__init__
()
self
.
eps_decay
=
eps_decay
self
.
min_eps
=
min_eps
self
.
decay_start
=
decay_start
self
.
decay_per_step
=
decay_per_step
self
.
last_episode
=
-
1
def
d
ecay
(
self
,
cur_eps
,
episode
):
if
episode
<
self
.
decay_start
:
return
cur_eps
def
d
o_decay
(
self
,
episode
):
if
self
.
decay_per_step
:
do
=
(
episode
>=
self
.
decay_start
)
else
:
do
=
((
self
.
last_episode
!=
episode
)
and
(
episode
>=
self
.
decay_start
))
self
.
last_episode
=
episode
return
do
def
decay
(
self
,
cur_eps
,
episode
):
if
self
.
do_decay
(
episode
):
return
max
(
cur_eps
-
self
.
eps_decay
,
self
.
min_eps
)
else
:
return
cur_eps
class
BaseStrategy
(
object
):
...
...
@@ -170,3 +190,29 @@ class OrnsteinUhlenbeckStrategy(BaseStrategy):
noise
=
self
.
_evolve_state
()
action
=
(
1.0
-
self
.
cur_eps
)
*
values
+
(
self
.
cur_eps
*
noise
)
return
np
.
clip
(
action
,
self
.
_action_low
,
self
.
_action_high
)
class
GaussianNoiseStrategy
(
BaseStrategy
):
def
__init__
(
self
,
action_dim
,
action_low
,
action_high
,
eps
,
noise_variance
,
decay
=
NoDecay
()
):
super
(
GaussianNoiseStrategy
,
self
).
__init__
(
decay
)
self
.
eps
=
eps
self
.
cur_eps
=
eps
self
.
_action_dim
=
action_dim
self
.
_action_low
=
action_low
self
.
_action_high
=
action_high
self
.
_noise_variance
=
noise_variance
def
select_action
(
self
,
values
):
noise
=
np
.
random
.
normal
(
loc
=
0.0
,
scale
=
self
.
_noise_variance
,
size
=
self
.
_action_dim
)
action
=
values
+
self
.
cur_eps
*
noise
return
np
.
clip
(
action
,
self
.
_action_low
,
self
.
_action_high
)
src/test/resources/target_code/gluon/reinforcementModel/torcs/reinforcement_learning/util.py
View file @
869ec892
...
...
@@ -11,8 +11,8 @@ import cnnarch_logger
LOSS_FUNCTIONS
=
{
'l1'
:
gluon
.
loss
.
L1Loss
(),
'
euclidean
'
:
gluon
.
loss
.
L2Loss
(),
'huber
_loss
'
:
gluon
.
loss
.
HuberLoss
(),
'
l2
'
:
gluon
.
loss
.
L2Loss
(),
'huber'
:
gluon
.
loss
.
HuberLoss
(),
'softmax_cross_entropy'
:
gluon
.
loss
.
SoftmaxCrossEntropyLoss
(),
'sigmoid_cross_entropy'
:
gluon
.
loss
.
SigmoidBinaryCrossEntropyLoss
()}
...
...
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