rllib/agents/trainer_config.py
def training(
        self,
        gamma: Optional[float] = None,
        lr: Optional[float] = None,
        train_batch_size: Optional[int] = None,
        model: Optional[dict] = None,
        optimizer: Optional[dict] = None,
    ) -> "TrainerConfig":
        """Sets the training related configuration.

        Args:
            gamma: Float specifying the discount factor of the Markov Decision process.
            lr: The default learning rate.
            train_batch_size: Training batch size, if applicable.
            model: Arguments passed into the policy model. See models/catalog.py for a
                full list of the available model options.
            optimizer: Arguments to pass to the policy optimizer.

        Returns:
            This updated TrainerConfig object.
        """
        if gamma is not None:
            self.gamma = gamma
        if lr is not None:
            self.lr = lr
        if train_batch_size is not None:
            self.train_batch_size = train_batch_size
        if model is not None:
            self.model = model
        if optimizer is not None:
            self.optimizer = optimizer

        return self
Sets the training related configuration.

        Args:
            gamma: Float specifying the discount factor of the Markov Decision process.
            lr: The default learning rate.
            train_batch_size: Training batch size, if applicable.
            model: Arguments passed into the policy model. See models/catalog.py for a
                full list of the available model options.
            optimizer: Arguments to pass to the policy optimizer.

        Returns:
            This updated TrainerConfig object.
        
Sets the training related configuration.
Sets the training related configuration .
def training(
        self,
        gamma: Optional[float] = None,
        lr: Optional[float] = None,
        train_batch_size: Optional[int] = None,
        model: Optional[dict] = None,
        optimizer: Optional[dict] = None,
    ) -> "TrainerConfig":
        if gamma is not None:
            self.gamma = gamma
        if lr is not None:
            self.lr = lr
        if train_batch_size is not None:
            self.train_batch_size = train_batch_size
        if model is not None:
            self.model = model
        if optimizer is not None:
            self.optimizer = optimizer
        return self
def training ( self , gamma : Optional [ float ] = None , lr : Optional [ float ] = None , train_batch_size : Optional [ int ] = None , model : Optional [ dict ] = None , optimizer : Optional [ dict ] = None , ) -> "TrainerConfig" : if gamma is not None : self . gamma = gamma if lr is not None : self . lr = lr if train_batch_size is not None : self . train_batch_size = train_batch_size if model is not None : self . model = model if optimizer is not None : self . optimizer = optimizer return self
Sets the training related configuration.
Sets the training related configuration .
"""Sets the training related configuration.

        Args:
            gamma: Float specifying the discount factor of the Markov Decision process.
            lr: The default learning rate.
            train_batch_size: Training batch size, if applicable.
            model: Arguments passed into the policy model. See models/catalog.py for a
                full list of the available model options.
            optimizer: Arguments to pass to the policy optimizer.

        Returns:
            This updated TrainerConfig object.
        """
This updated TrainerConfig object.
This updated TrainerConfig object .
self
gamma
Optional[float]
Float specifying the discount factor of the Markov Decision process.
Float specifying the discount factor of the Markov Decision process .
lr
Optional[float]
The default learning rate.
The default learning rate .
train_batch_size
Optional[int]
Training batch size, if applicable.
Training batch size if applicable .
model
Optional[dict]
Arguments passed into the policy model. See models/catalog.py for a
full list of the available model options.
Arguments passed into the policy model . See models / catalog . py for a full list of the available model options .
optimizer
Optional[dict]
Arguments to pass to the policy optimizer.
Arguments to pass to the policy optimizer .