refactor(reforcexy): use proper types

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/ReforceXY/user_data/freqaimodels/ReforceXY.py b/ReforceXY/user_data/freqaimodels/ReforceXY.py
index 7cb833ae07c9fc71b8713b50055fef671377ba2a..8360b117155210b1782cfe84812955b65ba252c1 100644 (file)
--- a/ReforceXY/user_data/freqaimodels/ReforceXY.py
+++ b/ReforceXY/user_data/freqaimodels/ReforceXY.py
@@ -150,26 +150,26 @@ class ReforceXY(BaseReinforcementLearningModel):
     _LOG_2: Final[float] = math.log(2.0)
     DEFAULT_IDLE_DURATION_MULTIPLIER: Final[int] = 4
 
-    _MODEL_TYPES: Final[tuple[ModelType, ...]] = (
+    _MODEL_TYPES: Final[Tuple[ModelType, ...]] = (
         "PPO",
         "RecurrentPPO",
         "MaskablePPO",
         "DQN",
         "QRDQN",
     )
-    _SCHEDULE_TYPES_KNOWN: Final[tuple[ScheduleTypeKnown, ...]] = ("linear", "constant")
-    _SCHEDULE_TYPES: Final[tuple[ScheduleType, ...]] = (
+    _SCHEDULE_TYPES_KNOWN: Final[Tuple[ScheduleTypeKnown, ...]] = ("linear", "constant")
+    _SCHEDULE_TYPES: Final[Tuple[ScheduleType, ...]] = (
         *_SCHEDULE_TYPES_KNOWN,
         "unknown",
     )
-    _EXIT_POTENTIAL_MODES: Final[tuple[ExitPotentialMode, ...]] = (
+    _EXIT_POTENTIAL_MODES: Final[Tuple[ExitPotentialMode, ...]] = (
         "canonical",
         "non_canonical",
         "progressive_release",
         "spike_cancel",
         "retain_previous",
     )
-    _TRANSFORM_FUNCTIONS: Final[tuple[TransformFunction, ...]] = (
+    _TRANSFORM_FUNCTIONS: Final[Tuple[TransformFunction, ...]] = (
         "tanh",
         "softsign",
         "arctan",
@@ -177,36 +177,36 @@ class ReforceXY(BaseReinforcementLearningModel):
         "asinh",
         "clip",
     )
-    _EXIT_ATTENUATION_MODES: Final[tuple[ExitAttenuationMode, ...]] = (
+    _EXIT_ATTENUATION_MODES: Final[Tuple[ExitAttenuationMode, ...]] = (
         "legacy",
         "sqrt",
         "linear",
         "power",
         "half_life",
     )
-    _ACTIVATION_FUNCTIONS: Final[tuple[ActivationFunction, ...]] = (
+    _ACTIVATION_FUNCTIONS: Final[Tuple[ActivationFunction, ...]] = (
         "relu",
         "tanh",
         "elu",
         "leaky_relu",
     )
-    _OPTIMIZER_CLASSES_OPTUNA: Final[tuple[OptimizerClassOptuna, ...]] = (
+    _OPTIMIZER_CLASSES_OPTUNA: Final[Tuple[OptimizerClassOptuna, ...]] = (
         "adamw",
         "rmsprop",
     )
-    _OPTIMIZER_CLASSES: Final[tuple[OptimizerClass, ...]] = (
+    _OPTIMIZER_CLASSES: Final[Tuple[OptimizerClass, ...]] = (
         *_OPTIMIZER_CLASSES_OPTUNA,
         "adam",
     )
-    _NET_ARCH_SIZES: Final[tuple[NetArchSize, ...]] = (
+    _NET_ARCH_SIZES: Final[Tuple[NetArchSize, ...]] = (
         "small",
         "medium",
         "large",
         "extra_large",
     )
-    _STORAGE_BACKENDS: Final[tuple[StorageBackend, ...]] = ("sqlite", "file")
-    _SAMPLER_TYPES: Final[tuple[SamplerType, ...]] = ("tpe", "auto")
-    _PPO_N_STEPS: Final[tuple[int, ...]] = (512, 1024, 2048, 4096)
+    _STORAGE_BACKENDS: Final[Tuple[StorageBackend, ...]] = ("sqlite", "file")
+    _SAMPLER_TYPES: Final[Tuple[SamplerType, ...]] = ("tpe", "auto")
+    _PPO_N_STEPS: Final[Tuple[int, ...]] = (512, 1024, 2048, 4096)
 
     _action_masks_cache: ClassVar[Dict[Tuple[bool, float], NDArray[np.bool_]]] = {}
 
@@ -3249,7 +3249,7 @@ class InfoMetricsCallback(TensorboardCallback):
                 hparam_dict.update({"train_freq": train_freq})
             if ReforceXY._MODEL_TYPES[4] in self.model.__class__.__name__:  # "QRDQN"
                 hparam_dict.update({"n_quantiles": int(self.model.n_quantiles)})
-        metric_dict: dict[str, float | int] = {
+        metric_dict: Dict[str, float | int] = {
             "eval/mean_reward": 0.0,
             "eval/mean_reward_std": 0.0,
             "rollout/ep_rew_mean": 0.0,