Docstring: minor changes to let ruff pass

docs/autogen_rst.py

@@ -39,6 +39,7 @@ def write_to_file(content: str, path: str):
 
 def make_rst(src_root, rst_root, clean=False, overwrite=False, package_prefix=""):
     """Creates/updates documentation in form of rst files for modules and packages.
+
     Does not delete any existing rst files. Thus, rst files for packages or modules that have been removed or renamed
     should be deleted by hand.
 
@@ -116,6 +117,6 @@ if __name__ == "__main__":
     docs_root = Path(__file__).parent
     make_rst(
         docs_root / ".." / "tianshou",
-        docs_root / "api",
+        docs_root / "03_api",
         clean=True,
     )

tianshou/data/buffer/base.py

@@ -18,7 +18,7 @@ class ReplayBuffer:
     stores all the data in a batch with circular-queue style.
 
     For the example usage of ReplayBuffer, please check out Section Buffer in
-    :doc:`/tutorials/01_concepts`.
+    :doc:`/01_tutorials/01_concepts`.
 
     :param size: the maximum size of replay buffer.
     :param stack_num: the frame-stack sampling argument, should be greater than or

tianshou/highlevel/env.py

@@ -81,8 +81,7 @@ class Environments(ToStringMixin, ABC):
         num_training_envs: int,
         num_test_envs: int,
     ) -> "Environments":
-        """Creates a suitable subtype instance from a factory function that creates a single instance and
-        the type of environment (continuous/discrete).
+        """Creates a suitable subtype instance from a factory function that creates a single instance and the type of environment (continuous/discrete).
 
         :param factory_fn: the factory for a single environment instance
         :param env_type: the type of environments created by `factory_fn`
@@ -115,8 +114,7 @@ class Environments(ToStringMixin, ABC):
         }
 
     def set_persistence(self, *p: Persistence) -> None:
-        """Associates the given persistence handlers which may persist and restore
-        environment-specific information.
+        """Associates the given persistence handlers which may persist and restore environment-specific information.
 
         :param p: persistence handlers
         """

tianshou/highlevel/experiment.py

@@ -188,7 +188,9 @@ class Experiment(ToStringMixin):
         experiment_name: str | None = None,
         logger_run_id: str | None = None,
     ) -> ExperimentResult:
-        """:param experiment_name: the experiment name, which corresponds to the directory (within the logging
+        """Run the experiment and return the results.
+
+        :param experiment_name: the experiment name, which corresponds to the directory (within the logging
             directory) where all results associated with the experiment will be saved.
             The name may contain path separators (i.e. `os.path.sep`, as used by `os.path.join`), in which case
             a nested directory structure will be created.
@@ -327,6 +329,7 @@ class ExperimentBuilder:
 
     def with_logger_factory(self, logger_factory: LoggerFactory) -> Self:
         """Allows to customize the logger factory to use.
+
         If this method is not called, the default logger factory :class:`LoggerFactoryDefault` will be used.
 
         :param logger_factory: the factory to use
@@ -346,6 +349,7 @@ class ExperimentBuilder:
 
     def with_optim_factory(self, optim_factory: OptimizerFactory) -> Self:
         """Allows to customize the gradient-based optimizer to use.
+
         By default, :class:`OptimizerFactoryAdam` will be used with default parameters.
 
         :param optim_factory: the optimizer factory
@@ -390,6 +394,7 @@ class ExperimentBuilder:
 
     def with_trainer_stop_callback(self, callback: TrainerStopCallback) -> Self:
         """Allows to define a callback that decides whether training shall stop early.
+
         The callback receives the undiscounted returns of the testing result.
 
         :param callback: the callback
@@ -435,6 +440,7 @@ class _BuilderMixinActorFactory(ActorFutureProviderProtocol):
 
     def with_actor_factory(self, actor_factory: ActorFactory) -> Self:
         """Allows to customize the actor component via the specification of a factory.
+
         If this function is not called, a default actor factory (with default parameters) will be used.
 
         :param actor_factory: the factory to use for the creation of the actor network
@@ -450,7 +456,9 @@ class _BuilderMixinActorFactory(ActorFutureProviderProtocol):
         continuous_unbounded: bool = False,
         continuous_conditioned_sigma: bool = False,
     ) -> Self:
-        """:param hidden_sizes: the sequence of hidden dimensions to use in the network structure
+        """Adds a default actor factory with the given parameters.
+
+        :param hidden_sizes: the sequence of hidden dimensions to use in the network structure
         :param continuous_unbounded: whether, for continuous action spaces, to apply tanh activation on final logits
         :param continuous_conditioned_sigma: whether, for continuous action spaces, the standard deviation of continuous actions (sigma)
             shall be computed from the input; if False, sigma is an independent parameter.
@@ -479,9 +487,7 @@ class _BuilderMixinActorFactory(ActorFutureProviderProtocol):
 
 
 class _BuilderMixinActorFactory_ContinuousGaussian(_BuilderMixinActorFactory):
-    """Specialization of the actor mixin where, in the continuous case, the actor component outputs
-    Gaussian distribution parameters.
-    """
+    """Specialization of the actor mixin where, in the continuous case, the actor component outputs Gaussian distribution parameters."""
 
     def __init__(self) -> None:
         super().__init__(ContinuousActorType.GAUSSIAN)
@@ -494,6 +500,7 @@ class _BuilderMixinActorFactory_ContinuousGaussian(_BuilderMixinActorFactory):
         continuous_conditioned_sigma: bool = False,
     ) -> Self:
         """Defines use of the default actor factory, allowing its parameters it to be customized.
+
         The default actor factory uses an MLP-style architecture.
 
         :param hidden_sizes: dimensions of hidden layers used by the network
@@ -523,6 +530,7 @@ class _BuilderMixinActorFactory_ContinuousDeterministic(_BuilderMixinActorFactor
         hidden_activation: ModuleType = torch.nn.ReLU,
     ) -> Self:
         """Defines use of the default actor factory, allowing its parameters it to be customized.
+
         The default actor factory uses an MLP-style architecture.
 
         :param hidden_sizes: dimensions of hidden layers used by the network
@@ -700,6 +708,7 @@ class _BuilderMixinCriticEnsembleFactory:
 
     def with_critic_ensemble_factory(self, factory: CriticEnsembleFactory) -> Self:
         """Specifies that the given factory shall be used for the critic ensemble.
+
         If unspecified, the default factory (:class:`CriticEnsembleFactoryDefault`) is used.
 
         :param factory: the critic ensemble factory

tianshou/highlevel/logger.py

@@ -19,7 +19,9 @@ class LoggerFactory(ToStringMixin, ABC):
         run_id: str | None,
         config_dict: dict,
     ) -> TLogger:
-        """:param log_dir: path to the directory in which log data is to be stored
+        """Creates the logger.
+
+        :param log_dir: path to the directory in which log data is to be stored
         :param experiment_name: the name of the job, which may contain `os.path.sep`
         :param run_id: a unique name, which, depending on the logging framework, may be used to identify the logger
         :param config_dict: a dictionary with data that is to be logged

tianshou/highlevel/module/actor.py

@@ -168,7 +168,9 @@ class ActorFactoryContinuousGaussianNet(ActorFactoryContinuous):
         conditioned_sigma: bool = False,
         activation: ModuleType = nn.ReLU,
     ):
-        """:param hidden_sizes: the sequence of hidden dimensions to use in the network structure
+        """For actors with Gaussian policies.
+
+        :param hidden_sizes: the sequence of hidden dimensions to use in the network structure
         :param unbounded: whether to apply tanh activation on final logits
         :param conditioned_sigma: if True, the standard deviation of continuous actions (sigma) is computed from the
             input; if False, sigma is an independent parameter
@@ -229,9 +231,7 @@ class ActorFactoryDiscreteNet(ActorFactory):
 
 
 class ActorFactoryTransientStorageDecorator(ActorFactory):
-    """Wraps an actor factory, storing the most recently created actor instance such that it
-    can be retrieved.
-    """
+    """Wraps an actor factory, storing the most recently created actor instance such that it can be retrieved."""
 
     def __init__(self, actor_factory: ActorFactory, actor_future: ActorFuture):
         self.actor_factory = actor_factory

tianshou/highlevel/optim.py

@@ -20,7 +20,9 @@ class OptimizerFactory(ABC, ToStringMixin):
 
 class OptimizerFactoryTorch(OptimizerFactory):
     def __init__(self, optim_class: OptimizerWithLearningRateProtocol, **kwargs: Any):
-        """:param optim_class: the optimizer class (e.g. subclass of `torch.optim.Optimizer`),
+        """Factory for torch optimizers.
+
+        :param optim_class: the optimizer class (e.g. subclass of `torch.optim.Optimizer`),
             which will be passed the module parameters, the learning rate as `lr` and the
             kwargs provided.
         :param kwargs: keyword arguments to provide at optimizer construction

tianshou/highlevel/params/noise.py

@@ -12,13 +12,12 @@ class NoiseFactory(ToStringMixin, ABC):
 
 
 class NoiseFactoryMaxActionScaledGaussian(NoiseFactory):
-    """Factory for Gaussian noise where the standard deviation is a fraction of the maximum action value.
-
-    This factory can only be applied to continuous action spaces.
-    """
-
     def __init__(self, std_fraction: float):
-        """:param std_fraction: fraction (between 0 and 1) of the maximum action value that shall
+        """Factory for Gaussian noise where the standard deviation is a fraction of the maximum action value.
+
+        This factory can only be applied to continuous action spaces.
+
+        :param std_fraction: fraction (between 0 and 1) of the maximum action value that shall
         be used as the standard deviation
         """
         self.std_fraction = std_fraction

tianshou/highlevel/params/policy_params.py

@@ -43,7 +43,9 @@ class ParamTransformerData:
 
 
 class ParamTransformer(ABC):
-    """Transforms one or more parameters from the representation used by the high-level API
+    """Base class for parameter transformations from high to low-level API.
+
+    Transforms one or more parameters from the representation used by the high-level API
     to the representation required by the (low-level) policy implementation.
     It operates directly on a dictionary of keyword arguments, which is initially
     generated from the parameter dataclass (subclass of `Params`).
@@ -83,7 +85,9 @@ class ParamTransformerChangeValue(ParamTransformer):
 
 
 class ParamTransformerLRScheduler(ParamTransformer):
-    """Transforms a key containing a learning rate scheduler factory (removed) into a key containing
+    """Transformer for learning rate scheduler params.
+
+    Transforms a key containing a learning rate scheduler factory (removed) into a key containing
     a learning rate scheduler (added) for the data member `optim`.
     """
 
@@ -100,12 +104,12 @@ class ParamTransformerLRScheduler(ParamTransformer):
 
 
 class ParamTransformerMultiLRScheduler(ParamTransformer):
-    """Transforms several scheduler factories into a single scheduler, which may be a MultipleLRSchedulers instance
-    if more than one factory is indeed given.
-    """
-
     def __init__(self, optim_key_list: list[tuple[torch.optim.Optimizer, str]], key_scheduler: str):
-        """:param optim_key_list: a list of tuples (optimizer, key of learning rate factory)
+        """Transforms several scheduler factories into a single scheduler.
+
+         The result may be a `MultipleLRSchedulers` instance if more than one factory is indeed given.
+
+        :param optim_key_list: a list of tuples (optimizer, key of learning rate factory)
         :param key_scheduler: the key under which to store the resulting learning rate scheduler
         """
         self.optim_key_list = optim_key_list

tianshou/highlevel/persistence.py

@@ -57,9 +57,9 @@ class PersistenceGroup(Persistence):
 
 
 class PolicyPersistence:
-    """Handles persistence of the policy."""
-
     class Mode(Enum):
+        """Mode of persistence."""
+
         POLICY_STATE_DICT = "policy_state_dict"
         """Persist only the policy's state dictionary. Note that for a policy to be restored from
         such a dictionary, it is necessary to first create a structurally equivalent object which can
@@ -81,7 +81,9 @@ class PolicyPersistence:
         enabled: bool = True,
         mode: Mode = Mode.POLICY,
     ):
-        """:param additional_persistence: a persistence instance which is to be invoked whenever
+        """Handles persistence of the policy.
+
+        :param additional_persistence: a persistence instance which is to be invoked whenever
             this object is used to persist/restore data
         :param enabled: whether persistence is enabled (restoration is always enabled)
         :param mode: the persistence mode

tianshou/highlevel/trainer.py

@@ -51,7 +51,9 @@ class TrainerStopCallback(ToStringMixin, ABC):
 
     @abstractmethod
     def should_stop(self, mean_rewards: float, context: TrainingContext) -> bool:
-        """:param mean_rewards: the average undiscounted returns of the testing result
+        """Determines whether training should stop.
+
+        :param mean_rewards: the average undiscounted returns of the testing result
         :param context: the training context
         :return: True if the goal has been reached and training should stop, False otherwise
         """

tianshou/policy/base.py

@@ -159,8 +159,7 @@ class BasePolicy(ABC, nn.Module):
         info: dict[str, Any] | None = None,
         state: dict | BatchProtocol | np.ndarray | None = None,
     ) -> np.ndarray | int:
-        """Get action as int (for discrete env's) or array (for continuous ones) from
-        an env's observation and info.
+        """Get action as int (for discrete env's) or array (for continuous ones) from an env's observation and info.
 
         :param obs: observation from the gym's env.
         :param info: information given by the gym's env.

tianshou/utils/string.py

@@ -16,6 +16,8 @@ reCommaWhitespacePotentiallyBreaks = re.compile(r",\s+")
 
 log = logging.getLogger(__name__)
 
+# ruff: noqa
+
 
 class StringConverter(ABC):
     """Abstraction for a string conversion mechanism."""