Model APIs#

ModelV2 API (rllib.env.models.modelv2.ModelV2)#

All RLlib neural network models have to be provided as ModelV2 sub-classes.

class ray.rllib.models.modelv2.ModelV2(obs_space: <MagicMock name='mock.Space' id='140490861621840'>, action_space: <MagicMock name='mock.Space' id='140490861621840'>, num_outputs: int, model_config: dict, name: str, framework: str)[source]#

Defines an abstract neural network model for use with RLlib.

Custom models should extend either TFModelV2 or TorchModelV2 instead of this class directly.

Data flow:

obs -> forward() -> model_out: -> value_function() -> V(s)

get_initial_state() → List[Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]][source]#

Get the initial recurrent state values for the model.

Returns: List of np.array (for tf) or Tensor (for torch) objects containing the initial hidden state of an RNN, if applicable.

Examples

>>> import numpy as np
>>> from ray.rllib.models.modelv2 import ModelV2
>>> class MyModel(ModelV2): 
...     # ...
...     def get_initial_state(self):
...         return [
...             np.zeros(self.cell_size, np.float32),
...             np.zeros(self.cell_size, np.float32),
...         ]

forward(input_dict: Dict[str, Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]], state: List[Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]], seq_lens: Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]) -> (typing.Union[<built-in function array>, ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')], typing.List[typing.Union[<built-in function array>, ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]])[source]#

Call the model with the given input tensors and state.

Any complex observations (dicts, tuples, etc.) will be unpacked by __call__ before being passed to forward(). To access the flattened observation tensor, refer to input_dict[“obs_flat”].

This method can be called any number of times. In eager execution, each call to forward() will eagerly evaluate the model. In symbolic execution, each call to forward creates a computation graph that operates over the variables of this model (i.e., shares weights).

Custom models should override this instead of __call__.

Parameters

input_dict – dictionary of input tensors, including “obs”, “obs_flat”, “prev_action”, “prev_reward”, “is_training”, “eps_id”, “agent_id”, “infos”, and “t”.
state – list of state tensors with sizes matching those returned by get_initial_state + the batch dimension
seq_lens – 1d tensor holding input sequence lengths

Returns

A tuple consisting of the model output tensor of size [BATCH, num_outputs] and the list of new RNN state(s) if any.

Examples

>>> import numpy as np
>>> from ray.rllib.models.modelv2 import ModelV2
>>> class MyModel(ModelV2): 
...     # ...
>>>     def forward(self, input_dict, state, seq_lens):
>>>         model_out, self._value_out = self.base_model(
...             input_dict["obs"])
>>>         return model_out, state

value_function() → Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor][source]#

Returns the value function output for the most recent forward pass.

Note that a forward call has to be performed first, before this methods can return anything and thus that calling this method does not cause an extra forward pass through the network.

Returns: Value estimate tensor of shape [BATCH].

custom_loss(policy_loss: Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor], loss_inputs: Dict[str, Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]]) → Union[List[Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]], numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor][source]#

Override to customize the loss function used to optimize this model.

This can be used to incorporate self-supervised losses (by defining a loss over existing input and output tensors of this model), and supervised losses (by defining losses over a variable-sharing copy of this model’s layers).

You can find an runnable example in examples/custom_loss.py.

Parameters

policy_loss – List of or single policy loss(es) from the policy.
loss_inputs – map of input placeholders for rollout data.

Returns

List of or scalar tensor for the customized loss(es) for this model.

metrics() → Dict[str, Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]][source]#

Override to return custom metrics from your model.

The stats will be reported as part of the learner stats, i.e., info.learner.[policy_id, e.g. “default_policy”].model.key1=metric1

Returns: The custom metrics for this model.

import_from_h5(h5_file: str) → None[source]#

Imports weights from an h5 file.

Parameters: h5_file – The h5 file name to import weights from.

Example

>>> from ray.rllib.algorithms.ppo import PPO
>>> trainer = PPO(...)  
>>> trainer.import_policy_model_from_h5("/tmp/weights.h5") 
>>> for _ in range(10): 
>>>     trainer.train() 

last_output() → Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor][source]#: Returns the last output returned from calling the model.

context() → contextlib.AbstractContextManager[source]#: Returns a contextmanager for the current forward pass.

variables(as_dict: bool = False) → Union[List[Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]], Dict[str, Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]]][source]#

Returns the list (or a dict) of variables for this model.

Parameters: as_dict – Whether variables should be returned as dict-values (using descriptive str keys).
Returns: The list (or dict if as_dict is True) of all variables of this ModelV2.

trainable_variables(as_dict: bool = False) → Union[List[Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]], Dict[str, Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]]][source]#

Returns the list of trainable variables for this model.

Parameters: as_dict – Whether variables should be returned as dict-values (using descriptive keys).
Returns: The list (or dict if as_dict is True) of all trainable (tf)/requires_grad (torch) variables of this ModelV2.

is_time_major() → bool[source]#

If True, data for calling this ModelV2 must be in time-major format.

Returns: Whether this ModelV2 requires a time-major (TxBx…) data format.

RLlib comes with two sub-classes for TF (keras) models and PyTorch models:

TFModelV2 (rllib.env.models.tf.tf_modelv2.TFModelV2)#

class ray.rllib.models.tf.tf_modelv2.TFModelV2(obs_space: <MagicMock name='mock.spaces.Space' id='140494192538832'>, action_space: <MagicMock name='mock.spaces.Space' id='140494192538832'>, num_outputs: int, model_config: dict, name: str)[source]#

TF version of ModelV2, which should contain a tf keras Model.

Note that this class by itself is not a valid model unless you implement forward() in a subclass.

context() → contextlib.AbstractContextManager[source]#: Returns a contextmanager for the current TF graph.

update_ops() → List[Union[numpy.array, tf.Tensor, torch.Tensor]][source]#

Return the list of update ops for this model.

For example, this should include any BatchNorm update ops.

register_variables(variables: List[Union[numpy.array, tf.Tensor, torch.Tensor]]) → None[source]#: Register the given list of variables with this model.

variables(as_dict: bool = False) → Union[List[Union[numpy.array, tf.Tensor, torch.Tensor]], Dict[str, Union[numpy.array, tf.Tensor, torch.Tensor]]][source]#

Returns the list (or a dict) of variables for this model.

Parameters: as_dict – Whether variables should be returned as dict-values (using descriptive str keys).
Returns: The list (or dict if as_dict is True) of all variables of this ModelV2.

trainable_variables(as_dict: bool = False) → Union[List[Union[numpy.array, tf.Tensor, torch.Tensor]], Dict[str, Union[numpy.array, tf.Tensor, torch.Tensor]]][source]#

Returns the list of trainable variables for this model.

Parameters: as_dict – Whether variables should be returned as dict-values (using descriptive keys).
Returns: The list (or dict if as_dict is True) of all trainable (tf)/requires_grad (torch) variables of this ModelV2.

TorchModelV2 (rllib.env.models.torch.torch_modelv2.TorchModelV2)#

class ray.rllib.models.torch.torch_modelv2.TorchModelV2(obs_space: <MagicMock name='mock.spaces.Space' id='140494192538832'>, action_space: <MagicMock name='mock.spaces.Space' id='140494192538832'>, num_outputs: int, model_config: dict, name: str)[source]#

Torch version of ModelV2.

Note that this class by itself is not a valid model unless you inherit from nn.Module and implement forward() in a subclass.

variables(as_dict: bool = False) → Union[List[Union[numpy.array, tf.Tensor, torch.Tensor]], Dict[str, Union[numpy.array, tf.Tensor, torch.Tensor]]][source]#

Returns the list (or a dict) of variables for this model.

Parameters: as_dict – Whether variables should be returned as dict-values (using descriptive str keys).
Returns: The list (or dict if as_dict is True) of all variables of this ModelV2.

trainable_variables(as_dict: bool = False) → Union[List[Union[numpy.array, tf.Tensor, torch.Tensor]], Dict[str, Union[numpy.array, tf.Tensor, torch.Tensor]]][source]#

Returns the list of trainable variables for this model.

Parameters: as_dict – Whether variables should be returned as dict-values (using descriptive keys).
Returns: The list (or dict if as_dict is True) of all trainable (tf)/requires_grad (torch) variables of this ModelV2.

Ray 2.3.0

Model APIs

Contents

Model APIs#

ModelV2 API (rllib.env.models.modelv2.ModelV2)#

TFModelV2 (rllib.env.models.tf.tf_modelv2.TFModelV2)#

TorchModelV2 (rllib.env.models.torch.torch_modelv2.TorchModelV2)#