Source code for ray.rllib.models.catalog

from functools import partial
import gym
import logging
import numpy as np
import tree
from typing import List

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.modelv2 import ModelV2
from ray.rllib.models.preprocessors import get_preprocessor, Preprocessor
from import FullyConnectedNetwork
from import LSTM
from import make_v1_wrapper
from import LSTMWrapper
from import Categorical, \
    Deterministic, DiagGaussian, Dirichlet, \
    MultiActionDistribution, MultiCategorical
from import VisionNetwork
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 deprecation_warning, DEPRECATED_VALUE
from ray.rllib.utils.error import UnsupportedSpaceException
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.spaces.simplex import Simplex
from ray.rllib.utils.spaces.space_utils import flatten_space
from ray.rllib.utils.types import ModelConfigDict, TensorType

tf1, tf, tfv = try_import_tf()

logger = logging.getLogger(__name__)

# yapf: disable
# __sphinx_doc_begin__
MODEL_DEFAULTS: ModelConfigDict = {
    # === Built-in options ===
    # Filter config. List of [out_channels, kernel, stride] for each filter
    "conv_filters": None,
    # Nonlinearity for built-in convnet
    "conv_activation": "relu",
    # Nonlinearity for fully connected net (tanh, relu)
    "fcnet_activation": "tanh",
    # Number of hidden layers for fully connected net
    "fcnet_hiddens": [256, 256],
    # 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}, r_{t-1} to LSTM.
    "lstm_use_prev_action_reward": False,
    # When using modelv1 models with a modelv2 algorithm, you may have to
    # define the state shape here (e.g., [256, 256]).
    "state_shape": None,

    # == Atari ==
    # Whether to enable framestack for Atari envs
    "framestack": True,
    # 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 in the Model's
    "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 config keys.
    "custom_options": DEPRECATED_VALUE,
# __sphinx_doc_end__
# yapf: enable

[docs]@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() """
[docs] @staticmethod @DeveloperAPI def get_action_dist(action_space: gym.Space, config: ModelConfigDict, dist_type: str = 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[str]): Identifier of the action distribution interpreted as a hint. framework (str): One of "tf", "tfe", or "torch". 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 = None config = config or MODEL_DEFAULTS # Custom distribution given. if config.get("custom_action_dist"): action_dist_name = config["custom_action_dist"] logger.debug( "Using custom action distribution {}".format(action_dist_name)) dist = _global_registry.get(RLLIB_ACTION_DIST, action_dist_name) # 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 = dist_type # Box space -> DiagGaussian OR Deterministic. elif isinstance(action_space, gym.spaces.Box): 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 = TorchDiagGaussian if framework == "torch" \ else DiagGaussian elif dist_type == "deterministic": dist = TorchDeterministic if framework == "torch" \ else Deterministic # Discrete Space -> Categorical. elif isinstance(action_space, gym.spaces.Discrete): dist = TorchCategorical if framework == "torch" else Categorical # Tuple/Dict Spaces -> MultiAction. elif dist_type in (MultiActionDistribution, TorchMultiActionDistribution) or \ isinstance(action_space, (gym.spaces.Tuple, gym.spaces.Dict)): 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( (TorchMultiActionDistribution if framework == "torch" else MultiActionDistribution), action_space=action_space, child_distributions=child_dists, input_lens=input_lens), int(sum(input_lens)) # Simplex -> Dirichlet. elif isinstance(action_space, Simplex): if framework == "torch": # TODO(sven): implement raise NotImplementedError( "Simplex action spaces not supported for torch.") dist = Dirichlet # MultiDiscrete -> MultiCategorical. elif isinstance(action_space, gym.spaces.MultiDiscrete): dist = TorchMultiCategorical if framework == "torch" else \ MultiCategorical return partial(dist, 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, dist.required_model_output_shape(action_space, config)
[docs] @staticmethod @DeveloperAPI def get_action_shape(action_space: gym.Space) -> (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. Returns: (dtype, shape): Dtype and shape of the actions tensor. """ if isinstance(action_space, gym.spaces.Discrete): return (tf.int64, (None, )) elif isinstance(action_space, (gym.spaces.Box, Simplex)): return (tf.float32, (None, ) + action_space.shape) elif isinstance(action_space, gym.spaces.MultiDiscrete): return (tf.as_dtype(action_space.dtype), (None, ) + action_space.shape) elif isinstance(action_space, (gym.spaces.Tuple, gym.spaces.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], gym.spaces.Discrete): size += 1 else: all_discrete = False size += np.product(flat_action_space[i].shape) size = int(size) return (tf.int64 if all_discrete else tf.float32, (None, size)) else: raise NotImplementedError( "Action space {} not supported".format(action_space))
[docs] @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) return tf1.placeholder(dtype, shape=shape, name=name)
[docs] @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. framework (str): One of "tf", "tfe", or "torch". 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. """ if model_config.get("custom_model"): if "custom_options" in model_config and \ model_config["custom_options"] != DEPRECATED_VALUE: deprecation_warning( "model.custom_options", "model.custom_model_config", error=False) model_config["custom_model_config"] = \ model_config.pop("custom_options") 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"]) # TODO(sven): Hard-deprecate Model(V1). if issubclass(model_cls, ModelV2):"Wrapping {} as {}".format(model_cls, model_interface)) model_cls = ModelCatalog._wrap_if_needed( model_cls, model_interface) if framework in ["tf", "tfe"]: # Track and warn if vars were created but not registered. 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): # 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, **model_kwargs) except TypeError as e: # Keyword error: Try old way w/o kwargs. if "__init__() got an unexpected " in e.args[0]: logger.warning( "Custom ModelV2 should accept all custom " "options as **kwargs, instead of expecting" " them in config['custom_model_config']!") instance = model_cls(obs_space, action_space, num_outputs, model_config, name) # Other error -> re-raise. else: raise e registered = set(instance.variables()) not_registered = set() for var in created: if var not in registered: not_registered.add(var) if not_registered: raise ValueError( "It looks like variables {} were created as part " "of {} but does not appear in model.variables() " "({}). Did you forget to call " "model.register_variables() on the variables in " "question?".format(not_registered, instance, registered)) else: # PyTorch automatically tracks nn.Modules inside the parent # nn.Module's constructor. # TODO(sven): Do this for TF as well. instance = model_cls(obs_space, action_space, num_outputs, model_config, name, **model_kwargs) return instance # TODO(sven): Hard-deprecate Model(V1). This check will be # superflous then. elif tf.executing_eagerly(): raise ValueError( "Eager execution requires a TFModelV2 model to be " "used, however you specified a custom model {}".format( model_cls)) 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 model_config.get("use_lstm"): wrapped_cls = v2_class forward = wrapped_cls.forward v2_class = ModelCatalog._wrap_if_needed( wrapped_cls, LSTMWrapper) v2_class._wrapped_forward = forward # fallback to a default v1 model if v2_class is None: if tf.executing_eagerly(): raise ValueError( "Eager execution requires a TFModelV2 model to be " "used, however there is no default V2 model for this " "observation space: {}, use_lstm={}".format( obs_space, model_config.get("use_lstm"))) v2_class = make_v1_wrapper(ModelCatalog.get_model) # 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) elif framework == "torch": v2_class = \ default_model or ModelCatalog._get_v2_model_class( obs_space, model_config, framework=framework) if model_config.get("use_lstm"): from ray.rllib.models.torch.recurrent_net import LSTMWrapper \ as TorchLSTMWrapper wrapped_cls = v2_class forward = wrapped_cls.forward v2_class = ModelCatalog._wrap_if_needed( wrapped_cls, TorchLSTMWrapper) 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) else: raise NotImplementedError( "`framework` must be 'tf|tfe|torch', but is " "{}!".format(framework))
[docs] @staticmethod @DeveloperAPI def get_preprocessor(env: gym.Env, options: 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)
[docs] @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"]"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
[docs] @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)
[docs] @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. """ _global_registry.register(RLLIB_MODEL, model_name, model_class)
[docs] @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: assert issubclass(model_cls, ModelV2), model_cls if not model_interface or issubclass(model_cls, model_interface): return 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, model_config, framework="tf"): if framework == "torch": from ray.rllib.models.torch.fcnet import (FullyConnectedNetwork as FCNet) from ray.rllib.models.torch.visionnet import (VisionNetwork as VisionNet) else: from import \ FullyConnectedNetwork as FCNet from import \ VisionNetwork as VisionNet # Discrete/1D obs-spaces. if isinstance(input_space, gym.spaces.Discrete) or \ len(input_space.shape) <= 2: return FCNet # Default Conv2D net. else: return VisionNet # ------------------- # DEPRECATED METHODS. # -------------------
[docs] @staticmethod def get_model(input_dict, obs_space, action_space, num_outputs, options, state_in=None, seq_lens=None): """Deprecated: Use get_model_v2() instead.""" deprecation_warning("get_model", "get_model_v2", error=False) assert isinstance(input_dict, dict) options = options or MODEL_DEFAULTS model = ModelCatalog._get_model(input_dict, obs_space, action_space, num_outputs, options, state_in, seq_lens) if options.get("use_lstm"): copy = dict(input_dict) copy["obs"] = model.last_layer feature_space = gym.spaces.Box( -1, 1, shape=(model.last_layer.shape[1], )) model = LSTM(copy, feature_space, action_space, num_outputs, options, state_in, seq_lens) logger.debug( "Created model {}: ({} of {}, {}, {}, {}) -> {}, {}".format( model, input_dict, obs_space, action_space, state_in, seq_lens, model.outputs, model.state_out)) model._validate_output_shape() return model
@staticmethod def _get_model(input_dict, obs_space, action_space, num_outputs, options, state_in, seq_lens): deprecation_warning("_get_model", "get_model_v2", error=False) if options.get("custom_model"): model = options["custom_model"] logger.debug("Using custom model {}".format(model)) return _global_registry.get(RLLIB_MODEL, model)( input_dict, obs_space, action_space, num_outputs, options, state_in=state_in, seq_lens=seq_lens) obs_rank = len(input_dict["obs"].shape) - 1 # drops batch dim if obs_rank > 2: return VisionNetwork(input_dict, obs_space, action_space, num_outputs, options) return FullyConnectedNetwork(input_dict, obs_space, action_space, num_outputs, options)