def rl_track_setting(tmp_path):
# NOTE: Here instead of loading the `rl_track.yaml`, we create instantiate it
# directly, because we want to reduce the length of the task for testing, and it
# isn't currently possible to both pass a preset yaml file and also pass kwargs to
# the SettingProxy.
setting = SettingProxy(
IncrementalRLSetting,
dataset="monsterkong",
train_task_schedule={
0: {"level": 0},
1: {"level": 1},
2: {"level": 10},
3: {"level": 11},
4: {"level": 20},
5: {"level": 21},
6: {"level": 30},
7: {"level": 31},
},
steps_per_task=2_000, # Reduced length for testing
test_steps_per_task=2_000,
monitor_training_performance=True,
task_labels_at_train_time=True,
)
assert setting.steps_per_phase == 2000
assert sorted(setting.train_task_schedule.keys()) == list(range(0, 16_000, 2000))
return setting
def cartpole_state_setting():
setting = SettingProxy(
RLSetting,
dataset="cartpole",
max_steps=5_000,
test_steps=2_000,
monitor_training_performance=True,
)
return setting
def sl_track_setting():
setting = SettingProxy(
ClassIncrementalSetting,
"sl_track",
# dataset="synbols",
# nb_tasks=12,
# class_order=class_order,
)
return setting
def incremental_cartpole_state_setting():
setting = SettingProxy(
IncrementalRLSetting,
dataset="cartpole",
max_steps=10_000,
nb_tasks=2,
test_steps=2_000,
monitor_training_performance=True,
)
return setting
def sl_track_setting():
setting = SettingProxy(
ClassIncrementalSetting,
"sl_track",
# dataset="synbols",
# nb_tasks=12,
# class_order=class_order,
# monitor_training_performance=True,
)
return setting
def rl_track_setting():
setting = SettingProxy(
IncrementalRLSetting,
# "rl_track", # TODO: Levels 0-20 work for now in MonsterKong.
"rl_track",
steps_per_task=2_000, # just for testing.
test_steps_per_task=2_000, # just for testing.
# dataset="synbols",
# nb_tasks=12,
# class_order=class_order,
)
return setting
def run_track(method: Method, setting: Setting, yamlfile: str) -> Results:
setting = SettingProxy(setting, yamlfile)
results = setting.apply(method)
print(f"Results summary:\n" f"{results.summary()}")
print("=====================")
print(results.to_log_dict())
def mnist_setting():
return SettingProxy(
ClassIncrementalSetting,
dataset="mnist",
monitor_training_performance=True,
)
def mnist_setting():
return SettingProxy(
ClassIncrementalSetting,
dataset="mnist",
)
def fashion_mnist_setting():
return SettingProxy(
ClassIncrementalSetting,
dataset="fashionmnist",
)