Add ToStringMixin to further high-level parameter classes

tianshou/highlevel/module/core.py

@@ -8,6 +8,7 @@ import torch
 
 from tianshou.highlevel.env import Environments
 from tianshou.utils.net.common import Net
+from tianshou.utils.string import ToStringMixin
 
 TDevice: TypeAlias = str | int | torch.device
 
@@ -29,7 +30,7 @@ class Module:
     output_dim: int
 
 
-class ModuleFactory(ABC):
+class ModuleFactory(ToStringMixin, ABC):
     @abstractmethod
     def create_module(self, envs: Environments, device: TDevice) -> Module:
         pass

tianshou/highlevel/params/alpha.py

@@ -6,9 +6,10 @@ import torch
 from tianshou.highlevel.env import Environments
 from tianshou.highlevel.module.core import TDevice
 from tianshou.highlevel.optim import OptimizerFactory
+from tianshou.utils.string import ToStringMixin
 
 
-class AutoAlphaFactory(ABC):
+class AutoAlphaFactory(ToStringMixin, ABC):
     @abstractmethod
     def create_auto_alpha(
         self,

tianshou/highlevel/params/dist_fn.py

@@ -6,11 +6,12 @@ import torch
 
 from tianshou.highlevel.env import Environments, EnvType
 from tianshou.policy.modelfree.pg import TDistParams
+from tianshou.utils.string import ToStringMixin
 
 TDistributionFunction: TypeAlias = Callable[[TDistParams], torch.distributions.Distribution]
 
 
-class DistributionFunctionFactory(ABC):
+class DistributionFunctionFactory(ToStringMixin, ABC):
     @abstractmethod
     def create_dist_fn(self, envs: Environments) -> TDistributionFunction:
         pass

tianshou/highlevel/params/env_param.py

@@ -3,12 +3,13 @@ from abc import ABC, abstractmethod
 from typing import Generic, TypeVar
 
 from tianshou.highlevel.env import ContinuousEnvironments, Environments
+from tianshou.utils.string import ToStringMixin
 
 TValue = TypeVar("TValue")
 TEnvs = TypeVar("TEnvs", bound=Environments)
 
 
-class EnvValueFactory(Generic[TValue, TEnvs], ABC):
+class EnvValueFactory(Generic[TValue, TEnvs], ToStringMixin, ABC):
     @abstractmethod
     def create_value(self, envs: TEnvs) -> TValue:
         pass

tianshou/highlevel/params/lr_scheduler.py

@@ -5,9 +5,10 @@ import torch
 from torch.optim.lr_scheduler import LambdaLR, LRScheduler
 
 from tianshou.highlevel.config import RLSamplingConfig
+from tianshou.utils.string import ToStringMixin
 
 
-class LRSchedulerFactory(ABC):
+class LRSchedulerFactory(ToStringMixin, ABC):
     @abstractmethod
     def create_scheduler(self, optim: torch.optim.Optimizer) -> LRScheduler:
         pass

tianshou/highlevel/params/noise.py

@@ -2,9 +2,10 @@ from abc import ABC, abstractmethod
 
 from tianshou.exploration import BaseNoise, GaussianNoise
 from tianshou.highlevel.env import ContinuousEnvironments, Environments
+from tianshou.utils.string import ToStringMixin
 
 
-class NoiseFactory(ABC):
+class NoiseFactory(ToStringMixin, ABC):
     @abstractmethod
     def create_noise(self, envs: Environments) -> BaseNoise:
         pass

tianshou/highlevel/params/policy_wrapper.py

@@ -7,12 +7,13 @@ from tianshou.highlevel.module.core import ModuleFactory, TDevice
 from tianshou.highlevel.optim import OptimizerFactory
 from tianshou.policy import BasePolicy, ICMPolicy
 from tianshou.utils.net.discrete import IntrinsicCuriosityModule
+from tianshou.utils.string import ToStringMixin
 
 TPolicyIn = TypeVar("TPolicyIn", bound=BasePolicy)
 TPolicyOut = TypeVar("TPolicyOut", bound=BasePolicy)
 
 
-class PolicyWrapperFactory(Generic[TPolicyIn, TPolicyOut], ABC):
+class PolicyWrapperFactory(Generic[TPolicyIn, TPolicyOut], ToStringMixin, ABC):
     @abstractmethod
     def create_wrapped_policy(
         self,