Improve docstrings
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@ -187,7 +187,7 @@ class Experiment(ToStringMixin):
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) -> ExperimentResult:
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) -> ExperimentResult:
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""":param experiment_name: the experiment name, which corresponds to the directory (within the logging
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""":param experiment_name: the experiment name, which corresponds to the directory (within the logging
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directory) where all results associated with the experiment will be saved.
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directory) where all results associated with the experiment will be saved.
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The name may contain path separators (os.path.sep, used by os.path.join), in which case
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The name may contain path separators (i.e. `os.path.sep`, as used by `os.path.join`), in which case
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a nested directory structure will be created.
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a nested directory structure will be created.
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If None, use a name containing the current date and time.
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If None, use a name containing the current date and time.
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:param logger_run_id: Run identifier to use for logger initialization/resumption (applies when
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:param logger_run_id: Run identifier to use for logger initialization/resumption (applies when
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@ -20,7 +20,7 @@ class LoggerFactory(ToStringMixin, ABC):
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config_dict: dict,
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config_dict: dict,
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) -> TLogger:
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) -> TLogger:
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""":param log_dir: path to the directory in which log data is to be stored
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""":param log_dir: path to the directory in which log data is to be stored
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:param experiment_name: the name of the job, which may contain os.path.sep
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:param experiment_name: the name of the job, which may contain `os.path.sep`
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:param run_id: a unique name, which, depending on the logging framework, may be used to identify the logger
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:param run_id: a unique name, which, depending on the logging framework, may be used to identify the logger
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:param config_dict: a dictionary with data that is to be logged
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:param config_dict: a dictionary with data that is to be logged
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:return: the logger
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:return: the logger
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@ -9,7 +9,7 @@ from tianshou.utils.string import ToStringMixin
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class LRSchedulerFactory(ToStringMixin, ABC):
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class LRSchedulerFactory(ToStringMixin, ABC):
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"""Factory for the createion of a learning rate scheduler."""
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"""Factory for the creation of a learning rate scheduler."""
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@abstractmethod
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@abstractmethod
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def create_scheduler(self, optim: torch.optim.Optimizer) -> LRScheduler:
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def create_scheduler(self, optim: torch.optim.Optimizer) -> LRScheduler:
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