rllib/agents/trainer_config.py
def experimental(
        self,
        *,
        _tf_policy_handles_more_than_one_loss=None,
        _disable_preprocessor_api=None,
        _disable_action_flattening=None,
        _disable_execution_plan_api=None,
    ) -> "TrainerConfig":
        """Sets the config's experimental settings.

        Args:
            _tf_policy_handles_more_than_one_loss: Experimental flag.
                If True, TFPolicy will handle more than one loss/optimizer.
                Set this to True, if you would like to return more than
                one loss term from your `loss_fn` and an equal number of optimizers
                from your `optimizer_fn`. In the future, the default for this will be
                True.
            _disable_preprocessor_api: Experimental flag.
                If True, no (observation) preprocessor will be created and
                observations will arrive in model as they are returned by the env.
                In the future, the default for this will be True.
            _disable_action_flattening: Experimental flag.
                If True, RLlib will no longer flatten the policy-computed actions into
                a single tensor (for storage in SampleCollectors/output files/etc..),
                but leave (possibly nested) actions as-is. Disabling flattening affects:
                - SampleCollectors: Have to store possibly nested action structs.
                - Models that have the previous action(s) as part of their input.
                - Algorithms reading from offline files (incl. action information).
            _disable_execution_plan_api: Experimental flag.
                If True, the execution plan API will not be used. Instead,
                a Trainer's `training_iteration` method will be called as-is each
                training iteration.

        Returns:
            This updated TrainerConfig object.
        """
        if _tf_policy_handles_more_than_one_loss is not None:
            self._tf_policy_handles_more_than_one_loss = (
                _tf_policy_handles_more_than_one_loss
            )
        if _disable_preprocessor_api is not None:
            self._disable_preprocessor_api = _disable_preprocessor_api
        if _disable_action_flattening is not None:
            self._disable_action_flattening = _disable_action_flattening
        if _disable_execution_plan_api is not None:
            self._disable_execution_plan_api = _disable_execution_plan_api

        return self
Sets the config's experimental settings.

        Args:
            _tf_policy_handles_more_than_one_loss: Experimental flag.
                If True, TFPolicy will handle more than one loss/optimizer.
                Set this to True, if you would like to return more than
                one loss term from your `loss_fn` and an equal number of optimizers
                from your `optimizer_fn`. In the future, the default for this will be
                True.
            _disable_preprocessor_api: Experimental flag.
                If True, no (observation) preprocessor will be created and
                observations will arrive in model as they are returned by the env.
                In the future, the default for this will be True.
            _disable_action_flattening: Experimental flag.
                If True, RLlib will no longer flatten the policy-computed actions into
                a single tensor (for storage in SampleCollectors/output files/etc..),
                but leave (possibly nested) actions as-is. Disabling flattening affects:
                - SampleCollectors: Have to store possibly nested action structs.
                - Models that have the previous action(s) as part of their input.
                - Algorithms reading from offline files (incl. action information).
            _disable_execution_plan_api: Experimental flag.
                If True, the execution plan API will not be used. Instead,
                a Trainer's `training_iteration` method will be called as-is each
                training iteration.

        Returns:
            This updated TrainerConfig object.
        
Sets the config's experimental settings.
Sets the config ' s experimental settings .
def experimental(
        self,
        *,
        _tf_policy_handles_more_than_one_loss=None,
        _disable_preprocessor_api=None,
        _disable_action_flattening=None,
        _disable_execution_plan_api=None,
    ) -> "TrainerConfig":
        if _tf_policy_handles_more_than_one_loss is not None:
            self._tf_policy_handles_more_than_one_loss = (
                _tf_policy_handles_more_than_one_loss
            )
        if _disable_preprocessor_api is not None:
            self._disable_preprocessor_api = _disable_preprocessor_api
        if _disable_action_flattening is not None:
            self._disable_action_flattening = _disable_action_flattening
        if _disable_execution_plan_api is not None:
            self._disable_execution_plan_api = _disable_execution_plan_api
        return self
def experimental ( self , * , _tf_policy_handles_more_than_one_loss = None , _disable_preprocessor_api = None , _disable_action_flattening = None , _disable_execution_plan_api = None , ) -> "TrainerConfig" : if _tf_policy_handles_more_than_one_loss is not None : self . _tf_policy_handles_more_than_one_loss = ( _tf_policy_handles_more_than_one_loss ) if _disable_preprocessor_api is not None : self . _disable_preprocessor_api = _disable_preprocessor_api if _disable_action_flattening is not None : self . _disable_action_flattening = _disable_action_flattening if _disable_execution_plan_api is not None : self . _disable_execution_plan_api = _disable_execution_plan_api return self
Sets the config's experimental settings.
Sets the config ' s experimental settings .
"""Sets the config's experimental settings.

        Args:
            _tf_policy_handles_more_than_one_loss: Experimental flag.
                If True, TFPolicy will handle more than one loss/optimizer.
                Set this to True, if you would like to return more than
                one loss term from your `loss_fn` and an equal number of optimizers
                from your `optimizer_fn`. In the future, the default for this will be
                True.
            _disable_preprocessor_api: Experimental flag.
                If True, no (observation) preprocessor will be created and
                observations will arrive in model as they are returned by the env.
                In the future, the default for this will be True.
            _disable_action_flattening: Experimental flag.
                If True, RLlib will no longer flatten the policy-computed actions into
                a single tensor (for storage in SampleCollectors/output files/etc..),
                but leave (possibly nested) actions as-is. Disabling flattening affects:
                - SampleCollectors: Have to store possibly nested action structs.
                - Models that have the previous action(s) as part of their input.
                - Algorithms reading from offline files (incl. action information).
            _disable_execution_plan_api: Experimental flag.
                If True, the execution plan API will not be used. Instead,
                a Trainer's `training_iteration` method will be called as-is each
                training iteration.

        Returns:
            This updated TrainerConfig object.
        """
This updated TrainerConfig object.
This updated TrainerConfig object .
self
_tf_policy_handles_more_than_one_loss
Experimental flag.
If True, TFPolicy will handle more than one loss/optimizer.
Set this to True, if you would like to return more than
one loss term from your `loss_fn` and an equal number of optimizers
from your `optimizer_fn`. In the future, the default for this will be
True.
Experimental flag . If True TFPolicy will handle more than one loss / optimizer . Set this to True if you would like to return more than one loss term from your ` loss_fn ` and an equal number of optimizers from your ` optimizer_fn ` . In the future the default for this will be True .
_disable_preprocessor_api
Experimental flag.
If True, no (observation) preprocessor will be created and
observations will arrive in model as they are returned by the env.
In the future, the default for this will be True.
Experimental flag . If True no ( observation ) preprocessor will be created and observations will arrive in model as they are returned by the env . In the future the default for this will be True .
_disable_action_flattening
Experimental flag.
If True, RLlib will no longer flatten the policy-computed actions into
a single tensor (for storage in SampleCollectors/output files/etc..),
but leave (possibly nested) actions as-is. Disabling flattening affects:
SampleCollectors: Have to store possibly nested action structs.
Models that have the previous action(s) as part of their input.
Algorithms reading from offline files (incl. action information).
Experimental flag . If True RLlib will no longer flatten the policy - computed actions into a single tensor ( for storage in SampleCollectors / output files / etc .. ) but leave ( possibly nested ) actions as - is . Disabling flattening affects : SampleCollectors : Have to store possibly nested action structs . Models that have the previous action ( s ) as part of their input . Algorithms reading from offline files ( incl . action information ) .
_disable_execution_plan_api
Experimental flag.
If True, the execution plan API will not be used. Instead,
a Trainer's `training_iteration` method will be called as-is each
training iteration.
Experimental flag . If True the execution plan API will not be used . Instead a Trainer ' s ` training_iteration ` method will be called as - is each training iteration .