ray/rllib/models/catalog.py

from functools import partial
import gym
from gym.spaces import Box, Dict, Discrete, MultiDiscrete, Tuple
import logging
import numpy as np
import tree  # pip install dm_tree
from typing import List, Optional, Type, Union

from ray.tune.registry import RLLIB_MODEL, RLLIB_PREPROCESSOR, \
    RLLIB_ACTION_DIST, _global_registry
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.jax.jax_action_dist import JAXCategorical
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.preprocessors import get_preprocessor, Preprocessor
from ray.rllib.models.tf.tf_action_dist import Categorical, \
    Deterministic, DiagGaussian, Dirichlet, \
    MultiActionDistribution, MultiCategorical
from ray.rllib.models.torch.torch_action_dist import TorchCategorical, \
    TorchDeterministic, TorchDiagGaussian, \
    TorchMultiActionDistribution, TorchMultiCategorical
from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI
from ray.rllib.utils.deprecation import DEPRECATED_VALUE, \
    deprecation_warning
from ray.rllib.utils.error import UnsupportedSpaceException
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.spaces.simplex import Simplex
from ray.rllib.utils.spaces.space_utils import flatten_space
from ray.rllib.utils.typing import ModelConfigDict, TensorType

tf1, tf, tfv = try_import_tf()
torch, _ = try_import_torch()

logger = logging.getLogger(__name__)

# yapf: disable
# __sphinx_doc_begin__
MODEL_DEFAULTS: ModelConfigDict = {
    # Experimental flag.
    # If True, try to use a native (tf.keras.Model or torch.Module) default
    # model instead of our built-in ModelV2 defaults.
    # If False (default), use "classic" ModelV2 default models.
    # Note that this currently only works for:
    # 1) framework != torch AND
    # 2) fully connected and CNN default networks as well as
    # auto-wrapped LSTM- and attention nets.
    "_use_default_native_models": False,

    # === Built-in options ===
    # FullyConnectedNetwork (tf and torch): rllib.models.tf|torch.fcnet.py
    # These are used if no custom model is specified and the input space is 1D.
    # Number of hidden layers to be used.
    "fcnet_hiddens": [256, 256],
    # Activation function descriptor.
    # Supported values are: "tanh", "relu", "swish" (or "silu"),
    # "linear" (or None).
    "fcnet_activation": "tanh",

    # VisionNetwork (tf and torch): rllib.models.tf|torch.visionnet.py
    # These are used if no custom model is specified and the input space is 2D.
    # Filter config: List of [out_channels, kernel, stride] for each filter.
    # Example:
    # Use None for making RLlib try to find a default filter setup given the
    # observation space.
    "conv_filters": None,
    # Activation function descriptor.
    # Supported values are: "tanh", "relu", "swish" (or "silu"),
    # "linear" (or None).
    "conv_activation": "relu",

    # Some default models support a final FC stack of n Dense layers with given
    # activation:
    # - Complex observation spaces: Image components are fed through
    #   VisionNets, flat Boxes are left as-is, Discrete are one-hot'd, then
    #   everything is concated and pushed through this final FC stack.
    # - VisionNets (CNNs), e.g. after the CNN stack, there may be
    #   additional Dense layers.
    # - FullyConnectedNetworks will have this additional FCStack as well
    # (that's why it's empty by default).
    "post_fcnet_hiddens": [],
    "post_fcnet_activation": "relu",

    # For DiagGaussian action distributions, make the second half of the model
    # outputs floating bias variables instead of state-dependent. This only
    # has an effect is using the default fully connected net.
    "free_log_std": False,
    # Whether to skip the final linear layer used to resize the hidden layer
    # outputs to size `num_outputs`. If True, then the last hidden layer
    # should already match num_outputs.
    "no_final_linear": False,
    # Whether layers should be shared for the value function.
    "vf_share_layers": True,

    # == LSTM ==
    # Whether to wrap the model with an LSTM.
    "use_lstm": False,
    # Max seq len for training the LSTM, defaults to 20.
    "max_seq_len": 20,
    # Size of the LSTM cell.
    "lstm_cell_size": 256,
    # Whether to feed a_{t-1} to LSTM (one-hot encoded if discrete).
    "lstm_use_prev_action": False,
    # Whether to feed r_{t-1} to LSTM.
    "lstm_use_prev_reward": False,
    # Whether the LSTM is time-major (TxBx..) or batch-major (BxTx..).
    "_time_major": False,

    # == Attention Nets (experimental: torch-version is untested) ==
    # Whether to use a GTrXL ("Gru transformer XL"; attention net) as the
    # wrapper Model around the default Model.
    "use_attention": False,
    # The number of transformer units within GTrXL.
    # A transformer unit in GTrXL consists of a) MultiHeadAttention module and
    # b) a position-wise MLP.
    "attention_num_transformer_units": 1,
    # The input and output size of each transformer unit.
    "attention_dim": 64,
    # The number of attention heads within the MultiHeadAttention units.
    "attention_num_heads": 1,
    # The dim of a single head (within the MultiHeadAttention units).
    "attention_head_dim": 32,
    # The memory sizes for inference and training.
    "attention_memory_inference": 50,
    "attention_memory_training": 50,
    # The output dim of the position-wise MLP.
    "attention_position_wise_mlp_dim": 32,
    # The initial bias values for the 2 GRU gates within a transformer unit.
    "attention_init_gru_gate_bias": 2.0,
    # Whether to feed a_{t-n:t-1} to GTrXL (one-hot encoded if discrete).
    "attention_use_n_prev_actions": 0,
    # Whether to feed r_{t-n:t-1} to GTrXL.
    "attention_use_n_prev_rewards": 0,

    # == Atari ==
    # Which framestacking size to use for Atari envs.
    # "auto": Use a value of 4, but only if the env is an Atari env.
    # > 1: Use the trajectory view API in the default VisionNets to request the
    #      last n observations (single, grayscaled 84x84 image frames) as
    #      inputs. The time axis in the so provided observation tensors
    #      will come right after the batch axis (channels first format),
    #      e.g. BxTx84x84, where T=num_framestacks.
    # 0 or 1: No framestacking used.
    # Use the deprecated `framestack=True`, to disable the above behavor and to
    # enable legacy stacking behavior (w/o trajectory view API) instead.
    "num_framestacks": "auto",
    # Final resized frame dimension
    "dim": 84,
    # (deprecated) Converts ATARI frame to 1 Channel Grayscale image
    "grayscale": False,
    # (deprecated) Changes frame to range from [-1, 1] if true
    "zero_mean": True,

    # === Options for custom models ===
    # Name of a custom model to use
    "custom_model": None,
    # Extra options to pass to the custom classes. These will be available to
    # the Model's constructor in the model_config field. Also, they will be
    # attempted to be passed as **kwargs to ModelV2 models. For an example,
    # see rllib/models/[tf|torch]/attention_net.py.
    "custom_model_config": {},
    # Name of a custom action distribution to use.
    "custom_action_dist": None,
    # Custom preprocessors are deprecated. Please use a wrapper class around
    # your environment instead to preprocess observations.
    "custom_preprocessor": None,

    # Deprecated keys:
    # Use `lstm_use_prev_action` or `lstm_use_prev_reward` instead.
    "lstm_use_prev_action_reward": DEPRECATED_VALUE,
    # Use `num_framestacks` (int) instead.
    "framestack": True,
}
# __sphinx_doc_end__
# yapf: enable


@PublicAPI
class ModelCatalog:
    """Registry of models, preprocessors, and action distributions for envs.

    Examples:
        >>> prep = ModelCatalog.get_preprocessor(env)
        >>> observation = prep.transform(raw_observation)

        >>> dist_class, dist_dim = ModelCatalog.get_action_dist(
        ...     env.action_space, {})
        >>> model = ModelCatalog.get_model_v2(
        ...     obs_space, action_space, num_outputs, options)
        >>> dist = dist_class(model.outputs, model)
        >>> action = dist.sample()
    """

    @staticmethod
    @DeveloperAPI
    def get_action_dist(
            action_space: gym.Space,
            config: ModelConfigDict,
            dist_type: Optional[Union[str, Type[ActionDistribution]]] = None,
            framework: str = "tf",
            **kwargs) -> (type, int):
        """Returns a distribution class and size for the given action space.

        Args:
            action_space (Space): Action space of the target gym env.
            config (Optional[dict]): Optional model config.
            dist_type (Optional[Union[str, Type[ActionDistribution]]]):
                Identifier of the action distribution (str) interpreted as a
                hint or the actual ActionDistribution class to use.
            framework (str): One of "tf2", "tf", "tfe", "torch", or "jax".
            kwargs (dict): Optional kwargs to pass on to the Distribution's
                constructor.

        Returns:
            Tuple:
                - dist_class (ActionDistribution): Python class of the
                    distribution.
                - dist_dim (int): The size of the input vector to the
                    distribution.
        """

        dist_cls = None
        config = config or MODEL_DEFAULTS
        # Custom distribution given.
        if config.get("custom_action_dist"):
            custom_action_config = config.copy()
            action_dist_name = custom_action_config.pop("custom_action_dist")
            logger.debug(
                "Using custom action distribution {}".format(action_dist_name))
            dist_cls = _global_registry.get(RLLIB_ACTION_DIST,
                                            action_dist_name)
            return ModelCatalog._get_multi_action_distribution(
                dist_cls, action_space, custom_action_config, framework)

        # Dist_type is given directly as a class.
        elif type(dist_type) is type and \
                issubclass(dist_type, ActionDistribution) and \
                dist_type not in (
                MultiActionDistribution, TorchMultiActionDistribution):
            dist_cls = dist_type
        # Box space -> DiagGaussian OR Deterministic.
        elif isinstance(action_space, Box):
            if action_space.dtype.name.startswith("int"):
                low_ = np.min(action_space.low)
                high_ = np.max(action_space.high)
                assert np.all(action_space.low == low_)
                assert np.all(action_space.high == high_)
                dist_cls = TorchMultiCategorical if framework == "torch" \
                    else MultiCategorical
                num_cats = int(np.product(action_space.shape))
                return partial(
                    dist_cls,
                    input_lens=[high_ - low_ + 1 for _ in range(num_cats)],
                    action_space=action_space), num_cats * (high_ - low_ + 1)
            else:
                if len(action_space.shape) > 1:
                    raise UnsupportedSpaceException(
                        "Action space has multiple dimensions "
                        "{}. ".format(action_space.shape) +
                        "Consider reshaping this into a single dimension, "
                        "using a custom action distribution, "
                        "using a Tuple action space, or the multi-agent API.")
                # TODO(sven): Check for bounds and return SquashedNormal, etc..
                if dist_type is None:
                    dist_cls = TorchDiagGaussian if framework == "torch" \
                        else DiagGaussian
                elif dist_type == "deterministic":
                    dist_cls = TorchDeterministic if framework == "torch" \
                        else Deterministic
        # Discrete Space -> Categorical.
        elif isinstance(action_space, Discrete):
            dist_cls = TorchCategorical if framework == "torch" else \
                JAXCategorical if framework == "jax" else Categorical
        # Tuple/Dict Spaces -> MultiAction.
        elif dist_type in (MultiActionDistribution,
                           TorchMultiActionDistribution) or \
                isinstance(action_space, (Tuple, Dict)):
            return ModelCatalog._get_multi_action_distribution(
                (MultiActionDistribution
                 if framework == "tf" else TorchMultiActionDistribution),
                action_space, config, framework)
        # Simplex -> Dirichlet.
        elif isinstance(action_space, Simplex):
            if framework == "torch":
                # TODO(sven): implement
                raise NotImplementedError(
                    "Simplex action spaces not supported for torch.")
            dist_cls = Dirichlet
        # MultiDiscrete -> MultiCategorical.
        elif isinstance(action_space, MultiDiscrete):
            dist_cls = TorchMultiCategorical if framework == "torch" else \
                MultiCategorical
            return partial(dist_cls, input_lens=action_space.nvec), \
                int(sum(action_space.nvec))
        # Unknown type -> Error.
        else:
            raise NotImplementedError("Unsupported args: {} {}".format(
                action_space, dist_type))

        return dist_cls, dist_cls.required_model_output_shape(
            action_space, config)

    @staticmethod
    @DeveloperAPI
    def get_action_shape(action_space: gym.Space,
                         framework: str = "tf") -> (np.dtype, List[int]):
        """Returns action tensor dtype and shape for the action space.

        Args:
            action_space (Space): Action space of the target gym env.
            framework (str): The framework identifier. One of "tf" or "torch".

        Returns:
            (dtype, shape): Dtype and shape of the actions tensor.
        """
        dl_lib = torch if framework == "torch" else tf

        if isinstance(action_space, Discrete):
            return action_space.dtype, (None, )
        elif isinstance(action_space, (Box, Simplex)):
            return dl_lib.float32, (None, ) + action_space.shape
        elif isinstance(action_space, MultiDiscrete):
            return action_space.dtype, (None, ) + action_space.shape
        elif isinstance(action_space, (Tuple, Dict)):
            flat_action_space = flatten_space(action_space)
            size = 0
            all_discrete = True
            for i in range(len(flat_action_space)):
                if isinstance(flat_action_space[i], Discrete):
                    size += 1
                else:
                    all_discrete = False
                    size += np.product(flat_action_space[i].shape)
            size = int(size)
            return dl_lib.int64 if all_discrete else dl_lib.float32, \
                (None, size)
        else:
            raise NotImplementedError(
                "Action space {} not supported".format(action_space))

    @staticmethod
    @DeveloperAPI
    def get_action_placeholder(action_space: gym.Space,
                               name: str = "action") -> TensorType:
        """Returns an action placeholder consistent with the action space

        Args:
            action_space (Space): Action space of the target gym env.
            name (str): An optional string to name the placeholder by.
                Default: "action".

        Returns:
            action_placeholder (Tensor): A placeholder for the actions
        """

        dtype, shape = ModelCatalog.get_action_shape(
            action_space, framework="tf")

        return tf1.placeholder(dtype, shape=shape, name=name)

    @staticmethod
    @DeveloperAPI
    def get_model_v2(obs_space: gym.Space,
                     action_space: gym.Space,
                     num_outputs: int,
                     model_config: ModelConfigDict,
                     framework: str = "tf",
                     name: str = "default_model",
                     model_interface: type = None,
                     default_model: type = None,
                     **model_kwargs) -> ModelV2:
        """Returns a suitable model compatible with given spaces and output.

        Args:
            obs_space (Space): Observation space of the target gym env. This
                may have an `original_space` attribute that specifies how to
                unflatten the tensor into a ragged tensor.
            action_space (Space): Action space of the target gym env.
            num_outputs (int): The size of the output vector of the model.
            model_config (ModelConfigDict): The "model" sub-config dict
                within the Trainer's config dict.
            framework (str): One of "tf2", "tf", "tfe", "torch", or "jax".
            name (str): Name (scope) for the model.
            model_interface (cls): Interface required for the model
            default_model (cls): Override the default class for the model. This
                only has an effect when not using a custom model
            model_kwargs (dict): args to pass to the ModelV2 constructor

        Returns:
            model (ModelV2): Model to use for the policy.
        """

        # Validate the given config dict.
        ModelCatalog._validate_config(config=model_config, framework=framework)

        if model_config.get("custom_model"):
            # Allow model kwargs to be overridden / augmented by
            # custom_model_config.
            customized_model_kwargs = dict(
                model_kwargs, **model_config.get("custom_model_config", {}))

            if isinstance(model_config["custom_model"], type):
                model_cls = model_config["custom_model"]
            else:
                model_cls = _global_registry.get(RLLIB_MODEL,
                                                 model_config["custom_model"])

            # Only allow ModelV2 or native keras Models.
            if not issubclass(model_cls, ModelV2):
                if framework not in ["tf", "tf2", "tfe"] or \
                        not issubclass(model_cls, tf.keras.Model):
                    raise ValueError(
                        "`model_cls` must be a ModelV2 sub-class, but is"
                        " {}!".format(model_cls))

            logger.info("Wrapping {} as {}".format(model_cls, model_interface))
            model_cls = ModelCatalog._wrap_if_needed(model_cls,
                                                     model_interface)

            if framework in ["tf2", "tf", "tfe"]:
                # Try wrapping custom model with LSTM/attention, if required.
                if model_config.get("use_lstm") or \
                        model_config.get("use_attention"):
                    from ray.rllib.models.tf.attention_net import \
                        AttentionWrapper, Keras_AttentionWrapper
                    from ray.rllib.models.tf.recurrent_net import \
                        LSTMWrapper, Keras_LSTMWrapper

                    wrapped_cls = model_cls
                    # Wrapped (custom) model is itself a keras Model ->
                    # wrap with keras LSTM/GTrXL (attention) wrappers.
                    if issubclass(wrapped_cls, tf.keras.Model):
                        model_cls = Keras_LSTMWrapper if \
                            model_config.get("use_lstm") else \
                            Keras_AttentionWrapper
                        model_config["wrapped_cls"] = wrapped_cls
                    # Wrapped (custom) model is ModelV2 ->
                    # wrap with ModelV2 LSTM/GTrXL (attention) wrappers.
                    else:
                        forward = wrapped_cls.forward
                        model_cls = ModelCatalog._wrap_if_needed(
                            wrapped_cls, LSTMWrapper if
                            model_config.get("use_lstm") else AttentionWrapper)
                        model_cls._wrapped_forward = forward

                # Obsolete: Track and warn if vars were created but not
                # registered. Only still do this, if users do register their
                # variables. If not (which they shouldn't), don't check here.
                created = set()

                def track_var_creation(next_creator, **kw):
                    v = next_creator(**kw)
                    created.add(v)
                    return v

                with tf.variable_creator_scope(track_var_creation):
                    if issubclass(model_cls, tf.keras.Model):
                        instance = model_cls(
                            input_space=obs_space,
                            action_space=action_space,
                            num_outputs=num_outputs,
                            name=name,
                            **customized_model_kwargs,
                        )
                    else:
                        # Try calling with kwargs first (custom ModelV2 should
                        # accept these as kwargs, not get them from
                        # config["custom_model_config"] anymore).
                        try:
                            instance = model_cls(
                                obs_space,
                                action_space,
                                num_outputs,
                                model_config,
                                name,
                                **customized_model_kwargs,
                            )
                        except TypeError as e:
                            # Keyword error: Try old way w/o kwargs.
                            if "__init__() got an unexpected " in e.args[0]:
                                instance = model_cls(
                                    obs_space,
                                    action_space,
                                    num_outputs,
                                    model_config,
                                    name,
                                    **model_kwargs,
                                )
                                logger.warning(
                                    "Custom ModelV2 should accept all custom "
                                    "options as **kwargs, instead of expecting"
                                    " them in config['custom_model_config']!")
                            # Other error -> re-raise.
                            else:
                                raise e

                # User still registered TFModelV2's variables: Check, whether
                # ok.
                registered = []
                if not isinstance(instance, tf.keras.Model):
                    registered = set(instance.var_list)
                if len(registered) > 0:
                    not_registered = set()
                    for var in created:
                        if var not in registered:
                            not_registered.add(var)
                    if not_registered:
                        raise ValueError(
                            "It looks like you are still using "
                            "`{}.register_variables()` to register your "
                            "model's weights. This is no longer required, but "
                            "if you are still calling this method at least "
                            "once, you must make sure to register all created "
                            "variables properly. The missing variables are {},"
                            " and you only registered {}. "
                            "Did you forget to call `register_variables()` on "
                            "some of the variables in question?".format(
                                instance, not_registered, registered))
            elif framework == "torch":
                # Try wrapping custom model with LSTM/attention, if required.
                if model_config.get("use_lstm") or \
                        model_config.get("use_attention"):
                    from ray.rllib.models.torch.attention_net import \
                        AttentionWrapper
                    from ray.rllib.models.torch.recurrent_net import \
                        LSTMWrapper

                    wrapped_cls = model_cls
                    forward = wrapped_cls.forward
                    model_cls = ModelCatalog._wrap_if_needed(
                        wrapped_cls, LSTMWrapper
                        if model_config.get("use_lstm") else AttentionWrapper)
                    model_cls._wrapped_forward = forward

                # PyTorch automatically tracks nn.Modules inside the parent
                # nn.Module's constructor.
                # Try calling with kwargs first (custom ModelV2 should
                # accept these as kwargs, not get them from
                # config["custom_model_config"] anymore).
                try:
                    instance = model_cls(obs_space, action_space, num_outputs,
                                         model_config, name,
                                         **customized_model_kwargs)
                except TypeError as e:
                    # Keyword error: Try old way w/o kwargs.
                    if "__init__() got an unexpected " in e.args[0]:
                        instance = model_cls(obs_space, action_space,
                                             num_outputs, model_config, name,
                                             **model_kwargs)
                        logger.warning(
                            "Custom ModelV2 should accept all custom "
                            "options as **kwargs, instead of expecting"
                            " them in config['custom_model_config']!")
                    # Other error -> re-raise.
                    else:
                        raise e
            else:
                raise NotImplementedError(
                    "`framework` must be 'tf2|tf|tfe|torch', but is "
                    "{}!".format(framework))

            return instance

        # Find a default TFModelV2 and wrap with model_interface.
        if framework in ["tf", "tfe", "tf2"]:
            v2_class = None
            # Try to get a default v2 model.
            if not model_config.get("custom_model"):
                v2_class = default_model or ModelCatalog._get_v2_model_class(
                    obs_space, model_config, framework=framework)

            if not v2_class:
                raise ValueError("ModelV2 class could not be determined!")

            if model_config.get("use_lstm") or \
                    model_config.get("use_attention"):

                from ray.rllib.models.tf.attention_net import \
                    AttentionWrapper, Keras_AttentionWrapper
                from ray.rllib.models.tf.recurrent_net import LSTMWrapper, \
                    Keras_LSTMWrapper

                wrapped_cls = v2_class
                if model_config.get("use_lstm"):
                    if issubclass(wrapped_cls, tf.keras.Model):
                        v2_class = Keras_LSTMWrapper
                        model_config["wrapped_cls"] = wrapped_cls
                    else:
                        v2_class = ModelCatalog._wrap_if_needed(
                            wrapped_cls, LSTMWrapper)
                        v2_class._wrapped_forward = wrapped_cls.forward
                else:
                    if issubclass(wrapped_cls, tf.keras.Model):
                        v2_class = Keras_AttentionWrapper
                        model_config["wrapped_cls"] = wrapped_cls
                    else:
                        v2_class = ModelCatalog._wrap_if_needed(
                            wrapped_cls, AttentionWrapper)
                        v2_class._wrapped_forward = wrapped_cls.forward

            # Wrap in the requested interface.
            wrapper = ModelCatalog._wrap_if_needed(v2_class, model_interface)

            if issubclass(wrapper, tf.keras.Model):
                model = wrapper(
                    input_space=obs_space,
                    action_space=action_space,
                    num_outputs=num_outputs,
                    name=name,
                    **dict(model_kwargs, **model_config),
                )
                return model

            return wrapper(obs_space, action_space, num_outputs, model_config,
                           name, **model_kwargs)

        # Find a default TorchModelV2 and wrap with model_interface.
        elif framework == "torch":
            # Try to get a default v2 model.
            if not model_config.get("custom_model"):
                v2_class = default_model or ModelCatalog._get_v2_model_class(
                    obs_space, model_config, framework=framework)

            if not v2_class:
                raise ValueError("ModelV2 class could not be determined!")

            if model_config.get("use_lstm") or \
                    model_config.get("use_attention"):

                from ray.rllib.models.torch.attention_net import \
                    AttentionWrapper
                from ray.rllib.models.torch.recurrent_net import LSTMWrapper

                wrapped_cls = v2_class
                forward = wrapped_cls.forward
                if model_config.get("use_lstm"):
                    v2_class = ModelCatalog._wrap_if_needed(
                        wrapped_cls, LSTMWrapper)
                else:
                    v2_class = ModelCatalog._wrap_if_needed(
                        wrapped_cls, AttentionWrapper)

                v2_class._wrapped_forward = forward

            # Wrap in the requested interface.
            wrapper = ModelCatalog._wrap_if_needed(v2_class, model_interface)
            return wrapper(obs_space, action_space, num_outputs, model_config,
                           name, **model_kwargs)

        # Find a default JAXModelV2 and wrap with model_interface.
        elif framework == "jax":
            v2_class = \
                default_model or ModelCatalog._get_v2_model_class(
                    obs_space, model_config, framework=framework)
            # Wrap in the requested interface.
            wrapper = ModelCatalog._wrap_if_needed(v2_class, model_interface)
            return wrapper(obs_space, action_space, num_outputs, model_config,
                           name, **model_kwargs)
        else:
            raise NotImplementedError(
                "`framework` must be 'tf2|tf|tfe|torch', but is "
                "{}!".format(framework))

    @staticmethod
    @DeveloperAPI
    def get_preprocessor(env: gym.Env,
                         options: Optional[dict] = None) -> Preprocessor:
        """Returns a suitable preprocessor for the given env.

        This is a wrapper for get_preprocessor_for_space().
        """

        return ModelCatalog.get_preprocessor_for_space(env.observation_space,
                                                       options)

    @staticmethod
    @DeveloperAPI
    def get_preprocessor_for_space(observation_space: gym.Space,
                                   options: dict = None) -> Preprocessor:
        """Returns a suitable preprocessor for the given observation space.

        Args:
            observation_space (Space): The input observation space.
            options (dict): Options to pass to the preprocessor.

        Returns:
            preprocessor (Preprocessor): Preprocessor for the observations.
        """

        options = options or MODEL_DEFAULTS
        for k in options.keys():
            if k not in MODEL_DEFAULTS:
                raise Exception("Unknown config key `{}`, all keys: {}".format(
                    k, list(MODEL_DEFAULTS)))

        if options.get("custom_preprocessor"):
            preprocessor = options["custom_preprocessor"]
            logger.info("Using custom preprocessor {}".format(preprocessor))
            logger.warning(
                "DeprecationWarning: Custom preprocessors are deprecated, "
                "since they sometimes conflict with the built-in "
                "preprocessors for handling complex observation spaces. "
                "Please use wrapper classes around your environment "
                "instead of preprocessors.")
            prep = _global_registry.get(RLLIB_PREPROCESSOR, preprocessor)(
                observation_space, options)
        else:
            cls = get_preprocessor(observation_space)
            prep = cls(observation_space, options)

        logger.debug("Created preprocessor {}: {} -> {}".format(
            prep, observation_space, prep.shape))
        return prep

    @staticmethod
    @PublicAPI
    def register_custom_preprocessor(preprocessor_name: str,
                                     preprocessor_class: type) -> None:
        """Register a custom preprocessor class by name.

        The preprocessor can be later used by specifying
        {"custom_preprocessor": preprocesor_name} in the model config.

        Args:
            preprocessor_name (str): Name to register the preprocessor under.
            preprocessor_class (type): Python class of the preprocessor.
        """
        _global_registry.register(RLLIB_PREPROCESSOR, preprocessor_name,
                                  preprocessor_class)

    @staticmethod
    @PublicAPI
    def register_custom_model(model_name: str, model_class: type) -> None:
        """Register a custom model class by name.

        The model can be later used by specifying {"custom_model": model_name}
        in the model config.

        Args:
            model_name (str): Name to register the model under.
            model_class (type): Python class of the model.
        """
        if tf is not None:
            if issubclass(model_class, tf.keras.Model):
                deprecation_warning(old="register_custom_model", error=False)
        _global_registry.register(RLLIB_MODEL, model_name, model_class)

    @staticmethod
    @PublicAPI
    def register_custom_action_dist(action_dist_name: str,
                                    action_dist_class: type) -> None:
        """Register a custom action distribution class by name.

        The model can be later used by specifying
        {"custom_action_dist": action_dist_name} in the model config.

        Args:
            model_name (str): Name to register the action distribution under.
            model_class (type): Python class of the action distribution.
        """
        _global_registry.register(RLLIB_ACTION_DIST, action_dist_name,
                                  action_dist_class)

    @staticmethod
    def _wrap_if_needed(model_cls: type, model_interface: type) -> type:
        if not model_interface or issubclass(model_cls, model_interface):
            return model_cls

        assert issubclass(model_cls, ModelV2), model_cls

        class wrapper(model_interface, model_cls):
            pass

        name = "{}_as_{}".format(model_cls.__name__, model_interface.__name__)
        wrapper.__name__ = name
        wrapper.__qualname__ = name

        return wrapper

    @staticmethod
    def _get_v2_model_class(input_space: gym.Space,
                            model_config: ModelConfigDict,
                            framework: str = "tf") -> Type[ModelV2]:

        VisionNet = None
        ComplexNet = None
        Keras_FCNet = None
        Keras_VisionNet = None

        if framework in ["tf2", "tf", "tfe"]:
            from ray.rllib.models.tf.fcnet import \
                FullyConnectedNetwork as FCNet, \
                Keras_FullyConnectedNetwork as Keras_FCNet
            from ray.rllib.models.tf.visionnet import \
                VisionNetwork as VisionNet, \
                Keras_VisionNetwork as Keras_VisionNet
            from ray.rllib.models.tf.complex_input_net import \
                ComplexInputNetwork as ComplexNet
        elif framework == "torch":
            from ray.rllib.models.torch.fcnet import (FullyConnectedNetwork as
                                                      FCNet)
            from ray.rllib.models.torch.visionnet import (VisionNetwork as
                                                          VisionNet)
            from ray.rllib.models.torch.complex_input_net import \
                ComplexInputNetwork as ComplexNet
        elif framework == "jax":
            from ray.rllib.models.jax.fcnet import (FullyConnectedNetwork as
                                                    FCNet)
        else:
            raise ValueError(
                "framework={} not supported in `ModelCatalog._get_v2_model_"
                "class`!".format(framework))

        # Discrete/1D obs-spaces or 2D obs space but traj. view framestacking
        # disabled.
        num_framestacks = model_config.get("num_framestacks", "auto")

        # Tuple space, where at least one sub-space is image.
        # -> Complex input model.
        space_to_check = input_space if not hasattr(
            input_space, "original_space") else input_space.original_space
        if isinstance(input_space,
                      Tuple) or (isinstance(space_to_check, Tuple) and any(
                          isinstance(s, Box) and len(s.shape) >= 2
                          for s in space_to_check.spaces)):
            return ComplexNet

        # Single, flattenable/one-hot-able space -> Simple FCNet.
        if isinstance(input_space, (Discrete, MultiDiscrete)) or \
                len(input_space.shape) == 1 or (
                len(input_space.shape) == 2 and (
                num_framestacks == "auto" or num_framestacks <= 1)):
            # Keras native requested AND no auto-rnn-wrapping.
            if model_config.get("_use_default_native_models") and Keras_FCNet:
                return Keras_FCNet
            # Classic ModelV2 FCNet.
            else:
                return FCNet

        elif framework == "jax":
            raise NotImplementedError("No non-FC default net for JAX yet!")

        # Last resort: Conv2D stack for single image spaces.
        if model_config.get("_use_default_native_models") and Keras_VisionNet:
            return Keras_VisionNet
        return VisionNet

    @staticmethod
    def _get_multi_action_distribution(dist_class, action_space, config,
                                       framework):
        # In case the custom distribution is a child of MultiActionDistr.
        # If users want to completely ignore the suggested child
        # distributions, they should simply do so in their custom class'
        # constructor.
        if issubclass(dist_class,
                      (MultiActionDistribution, TorchMultiActionDistribution)):
            flat_action_space = flatten_space(action_space)
            child_dists_and_in_lens = tree.map_structure(
                lambda s: ModelCatalog.get_action_dist(
                    s, config, framework=framework), flat_action_space)
            child_dists = [e[0] for e in child_dists_and_in_lens]
            input_lens = [int(e[1]) for e in child_dists_and_in_lens]
            return partial(
                dist_class,
                action_space=action_space,
                child_distributions=child_dists,
                input_lens=input_lens), int(sum(input_lens))
        return dist_class, dist_class.required_model_output_shape(
            action_space, config)

    @staticmethod
    def _validate_config(config: ModelConfigDict, framework: str) -> None:
        """Validates a given model config dict.

        Args:
            config (ModelConfigDict): The "model" sub-config dict
                within the Trainer's config dict.
            framework (str): One of "jax", "tf2", "tf", "tfe", or "torch".

        Raises:
            ValueError: If something is wrong with the given config.
        """
        if config.get("use_attention") and config.get("use_lstm"):
            raise ValueError("Only one of `use_lstm` or `use_attention` may "
                             "be set to True!")
        if framework == "jax":
            if config.get("use_attention"):
                raise ValueError("`use_attention` not available for "
                                 "framework=jax so far!")
            elif config.get("use_lstm"):
                raise ValueError("`use_lstm` not available for "
                                 "framework=jax so far!")

        if config.get("framestack") != DEPRECATED_VALUE:
            # deprecation_warning(
            #     old="framestack", new="num_framestacks (int)", error=False)
            # If old behavior is desired, disable traj. view-style
            # framestacking.
            config["num_framestacks"] = 0