def __init__( self, action_spec: types.BoundedTensorSpec, cov_matrix: Sequence[types.Float], data_vector: Sequence[types.Float], num_samples: Sequence[types.Int], time_step_spec: Optional[types.TimeStep] = None, exploration_strategy: ExplorationStrategy = ExplorationStrategy. optimistic, alpha: float = 1.0, eig_vals: Sequence[types.Float] = (), eig_matrix: Sequence[types.Float] = (), tikhonov_weight: float = 1.0, add_bias: bool = False, emit_policy_info: Sequence[Text] = (), emit_log_probability: bool = False, accepts_per_arm_features: bool = False, observation_and_action_constraint_splitter: Optional[ types.Splitter] = None, name: Optional[Text] = None): """Initializes `LinearBanditPolicy`. The `a` and `b` arguments may be either `Tensor`s or `tf.Variable`s. If they are variables, then any assignements to those variables will be reflected in the output of the policy. Args: action_spec: `TensorSpec` containing action specification. cov_matrix: list of the covariance matrices A in the paper. If the policy accepts per-arm features, the lenght of this list is 1, as there is only one model. Otherwise, there is one A matrix per arm. data_vector: list of the b vectors in the paper. The b vector is a weighted sum of the observations, where the weight is the corresponding reward. If the policy accepts per-arm features, this list should be of length 1, as there only 1 reward model maintained. Otherwise, each arm has its own vector b. num_samples: list of number of samples per arm, unless the policy accepts per-arm features, in which case this is just the number of samples seen. time_step_spec: A `TimeStep` spec of the expected time_steps. exploration_strategy: An Enum of type ExplortionStrategy. The strategy used for choosing the actions to incorporate exploration. Currently supported strategies are `optimistic` and `sampling`. alpha: a float value used to scale the confidence intervals. eig_vals: list of eigenvalues for each covariance matrix (one per arm, unless the policy accepts per-arm features). eig_matrix: list of eigenvectors for each covariance matrix (one per arm, unless the policy accepts per-arm features). tikhonov_weight: (float) tikhonov regularization term. add_bias: If true, a bias term will be added to the linear reward estimation. emit_policy_info: (tuple of strings) what side information we want to get as part of the policy info. Allowed values can be found in `policy_utilities.PolicyInfo`. emit_log_probability: Whether to emit log probabilities. accepts_per_arm_features: (bool) Whether the policy accepts per-arm features. observation_and_action_constraint_splitter: A function used for masking valid/invalid actions with each state of the environment. The function takes in a full observation and returns a tuple consisting of 1) the part of the observation intended as input to the bandit policy and 2) the mask. The mask should be a 0-1 `Tensor` of shape `[batch_size, num_actions]`. This function should also work with a `TensorSpec` as input, and should output `TensorSpec` objects for the observation and mask. name: The name of this policy. """ policy_utilities.check_no_mask_with_arm_features( accepts_per_arm_features, observation_and_action_constraint_splitter) if not isinstance(cov_matrix, (list, tuple)): raise ValueError( 'cov_matrix must be a list of matrices (Tensors).') self._cov_matrix = cov_matrix if not isinstance(data_vector, (list, tuple)): raise ValueError( 'data_vector must be a list of vectors (Tensors).') self._data_vector = data_vector if not isinstance(num_samples, (list, tuple)): raise ValueError( 'num_samples must be a list of vectors (Tensors).') self._num_samples = num_samples if not isinstance(eig_vals, (list, tuple)): raise ValueError('eig_vals must be a list of vectors (Tensors).') self._eig_vals = eig_vals if not isinstance(eig_matrix, (list, tuple)): raise ValueError('eig_matrix must be a list of vectors (Tensors).') self._eig_matrix = eig_matrix self._exploration_strategy = exploration_strategy if exploration_strategy == ExplorationStrategy.sampling: # We do not have a way to calculate log probabilities for TS yet. emit_log_probability = False self._alpha = alpha self._use_eigendecomp = False if eig_matrix: self._use_eigendecomp = True self._tikhonov_weight = tikhonov_weight self._add_bias = add_bias self._accepts_per_arm_features = accepts_per_arm_features if tf.nest.is_nested(action_spec): raise ValueError('Nested `action_spec` is not supported.') self._num_actions = action_spec.maximum + 1 self._check_input_variables() if observation_and_action_constraint_splitter is not None: context_spec, _ = observation_and_action_constraint_splitter( time_step_spec.observation) else: context_spec = time_step_spec.observation (self._global_context_dim, self._arm_context_dim) = bandit_spec_utils.get_context_dims_from_spec( context_spec, accepts_per_arm_features) if self._add_bias: # The bias is added via a constant 1 feature. self._global_context_dim += 1 self._overall_context_dim = self._global_context_dim + self._arm_context_dim cov_matrix_dim = tf.compat.dimension_value(cov_matrix[0].shape[0]) if self._overall_context_dim != cov_matrix_dim: raise ValueError('The dimension of matrix `cov_matrix` must match ' 'overall context dimension {}. ' 'Got {} for `cov_matrix`.'.format( self._overall_context_dim, cov_matrix_dim)) data_vector_dim = tf.compat.dimension_value(data_vector[0].shape[0]) if self._overall_context_dim != data_vector_dim: raise ValueError( 'The dimension of vector `data_vector` must match ' 'context dimension {}. ' 'Got {} for `data_vector`.'.format(self._overall_context_dim, data_vector_dim)) self._dtype = self._data_vector[0].dtype self._emit_policy_info = emit_policy_info info_spec = self._populate_policy_info_spec( time_step_spec.observation, observation_and_action_constraint_splitter) super(LinearBanditPolicy, self).__init__(time_step_spec=time_step_spec, action_spec=action_spec, info_spec=info_spec, emit_log_probability=emit_log_probability, observation_and_action_constraint_splitter=( observation_and_action_constraint_splitter), name=name)
def __init__( self, time_step_spec: Optional[ts.TimeStep], action_spec: Optional[types.NestedBoundedTensorSpec], scalarizer: multi_objective_scalarizer.Scalarizer, objective_networks: Sequence[Network], observation_and_action_constraint_splitter: types.Splitter = None, accepts_per_arm_features: bool = False, emit_policy_info: Tuple[Text] = (), name: Optional[Text] = None): """Builds a GreedyMultiObjectiveNeuralPolicy based on multiple networks. This policy takes an iterable of `tf_agents.Network`, each responsible for predicting a specific objective, along with a `Scalarizer` object to generate an action by maximizing the scalarized objective, i.e., the output of the `Scalarizer` applied to the multiple predicted objectives by the networks. Args: time_step_spec: A `TimeStep` spec of the expected time_steps. action_spec: A nest of `BoundedTensorSpec` representing the actions. scalarizer: A `tf_agents.bandits.multi_objective.multi_objective_scalarizer.Scalarizer` object that implements scalarization of multiple objectives into a single scalar reward. objective_networks: A Sequence of `tf_agents.network.Network` objects to be used by the policy. Each network will be called with call(observation, step_type) and is expected to provide a prediction for a specific objective for all actions. observation_and_action_constraint_splitter: A function used for masking valid/invalid actions with each state of the environment. The function takes in a full observation and returns a tuple consisting of 1) the part of the observation intended as input to the network and 2) the mask. The mask should be a 0-1 `Tensor` of shape `[batch_size, num_actions]`. This function should also work with a `TensorSpec` as input, and should output `TensorSpec` objects for the observation and mask. accepts_per_arm_features: (bool) Whether the policy accepts per-arm features. emit_policy_info: (tuple of strings) what side information we want to get as part of the policy info. Allowed values can be found in `policy_utilities.PolicyInfo`. name: The name of this policy. All variables in this module will fall under that name. Defaults to the class name. Raises: NotImplementedError: If `action_spec` contains more than one `BoundedTensorSpec` or the `BoundedTensorSpec` is not valid. NotImplementedError: If `action_spec` is not a `BoundedTensorSpec` of type int32 and shape (). ValueError: If `objective_networks` has fewer than two networks. ValueError: If `accepts_per_arm_features` is true but `time_step_spec` is None. """ policy_utilities.check_no_mask_with_arm_features( accepts_per_arm_features, observation_and_action_constraint_splitter) flat_action_spec = tf.nest.flatten(action_spec) if len(flat_action_spec) > 1: raise NotImplementedError( 'action_spec can only contain a single BoundedTensorSpec.') action_spec = flat_action_spec[0] if (not tensor_spec.is_bounded(action_spec) or not tensor_spec.is_discrete(action_spec) or action_spec.shape.rank > 1 or action_spec.shape.num_elements() != 1): raise NotImplementedError( 'action_spec must be a BoundedTensorSpec of type int32 and shape (). ' 'Found {}.'.format(action_spec)) self._expected_num_actions = action_spec.maximum - action_spec.minimum + 1 self._action_offset = action_spec.minimum policy_state_spec = [] for network in objective_networks: policy_state_spec.append(network.state_spec) network.create_variables() self._objective_networks = objective_networks self._scalarizer = scalarizer self._num_objectives = len(self._objective_networks) if self._num_objectives < 2: raise ValueError( 'Number of objectives should be at least two, but found to be {}' .format(self._num_objectives)) self._emit_policy_info = emit_policy_info predicted_rewards_mean = () if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN in emit_policy_info: predicted_rewards_mean = tensor_spec.TensorSpec( [self._num_objectives, self._expected_num_actions]) bandit_policy_type = () if policy_utilities.InfoFields.BANDIT_POLICY_TYPE in emit_policy_info: bandit_policy_type = ( policy_utilities.create_bandit_policy_type_tensor_spec( shape=[1])) if accepts_per_arm_features: if time_step_spec is None: raise ValueError( 'time_step_spec should not be None for per-arm-features policies, ' 'but found to be.') # The features for the chosen arm is saved to policy_info. chosen_arm_features_info = ( policy_utilities.create_chosen_arm_features_info_spec( time_step_spec.observation)) info_spec = policy_utilities.PerArmPolicyInfo( predicted_rewards_mean=predicted_rewards_mean, bandit_policy_type=bandit_policy_type, chosen_arm_features=chosen_arm_features_info) else: info_spec = policy_utilities.PolicyInfo( predicted_rewards_mean=predicted_rewards_mean, bandit_policy_type=bandit_policy_type) self._accepts_per_arm_features = accepts_per_arm_features super(GreedyMultiObjectiveNeuralPolicy, self).__init__(time_step_spec, action_spec, policy_state_spec=policy_state_spec, clip=False, info_spec=info_spec, emit_log_probability='log_probability' in emit_policy_info, observation_and_action_constraint_splitter=( observation_and_action_constraint_splitter), name=name)
def __init__(self, time_step_spec: types.TimeStep, action_spec: types.NestedTensorSpec, reward_network: types.Network, observation_and_action_constraint_splitter: Optional[ types.Splitter] = None, accepts_per_arm_features: bool = False, constraints: Tuple[constr.NeuralConstraint, ...] = (), emit_policy_info: Tuple[Text, ...] = (), name: Optional[Text] = None): """Builds a GreedyRewardPredictionPolicy given a reward tf_agents.Network. This policy takes a tf_agents.Network predicting rewards and generates the action corresponding to the largest predicted reward. Args: time_step_spec: A `TimeStep` spec of the expected time_steps. action_spec: A nest of BoundedTensorSpec representing the actions. reward_network: An instance of a `tf_agents.network.Network`, callable via `network(observation, step_type) -> (output, final_state)`. observation_and_action_constraint_splitter: A function used for masking valid/invalid actions with each state of the environment. The function takes in a full observation and returns a tuple consisting of 1) the part of the observation intended as input to the network and 2) the mask. The mask should be a 0-1 `Tensor` of shape `[batch_size, num_actions]`. This function should also work with a `TensorSpec` as input, and should output `TensorSpec` objects for the observation and mask. accepts_per_arm_features: (bool) Whether the policy accepts per-arm features. constraints: iterable of constraints objects that are instances of `tf_agents.bandits.agents.NeuralConstraint`. emit_policy_info: (tuple of strings) what side information we want to get as part of the policy info. Allowed values can be found in `policy_utilities.PolicyInfo`. name: The name of this policy. All variables in this module will fall under that name. Defaults to the class name. Raises: NotImplementedError: If `action_spec` contains more than one `BoundedTensorSpec` or the `BoundedTensorSpec` is not valid. """ policy_utilities.check_no_mask_with_arm_features( accepts_per_arm_features, observation_and_action_constraint_splitter) flat_action_spec = tf.nest.flatten(action_spec) if len(flat_action_spec) > 1: raise NotImplementedError( 'action_spec can only contain a single BoundedTensorSpec.') action_spec = flat_action_spec[0] if (not tensor_spec.is_bounded(action_spec) or not tensor_spec.is_discrete(action_spec) or action_spec.shape.rank > 1 or action_spec.shape.num_elements() != 1): raise NotImplementedError( 'action_spec must be a BoundedTensorSpec of type int32 and shape (). ' 'Found {}.'.format(action_spec)) self._expected_num_actions = action_spec.maximum - action_spec.minimum + 1 self._action_offset = action_spec.minimum reward_network.create_variables() self._reward_network = reward_network self._constraints = constraints self._emit_policy_info = emit_policy_info predicted_rewards_mean = () if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN in emit_policy_info: predicted_rewards_mean = tensor_spec.TensorSpec( [self._expected_num_actions]) bandit_policy_type = () if policy_utilities.InfoFields.BANDIT_POLICY_TYPE in emit_policy_info: bandit_policy_type = ( policy_utilities.create_bandit_policy_type_tensor_spec( shape=[1])) if accepts_per_arm_features: # The features for the chosen arm is saved to policy_info. chosen_arm_features_info = ( policy_utilities.create_chosen_arm_features_info_spec( time_step_spec.observation)) info_spec = policy_utilities.PerArmPolicyInfo( predicted_rewards_mean=predicted_rewards_mean, bandit_policy_type=bandit_policy_type, chosen_arm_features=chosen_arm_features_info) else: info_spec = policy_utilities.PolicyInfo( predicted_rewards_mean=predicted_rewards_mean, bandit_policy_type=bandit_policy_type) self._accepts_per_arm_features = accepts_per_arm_features super(GreedyRewardPredictionPolicy, self).__init__(time_step_spec, action_spec, policy_state_spec=reward_network.state_spec, clip=False, info_spec=info_spec, emit_log_probability='log_probability' in emit_policy_info, observation_and_action_constraint_splitter=( observation_and_action_constraint_splitter), name=name)
def __init__(self, encoding_network: types.Network, encoding_dim: int, reward_layer: tf.keras.layers.Dense, epsilon_greedy: float, actions_from_reward_layer: types.Bool, cov_matrix: Sequence[types.Float], data_vector: Sequence[types.Float], num_samples: Sequence[types.Int], time_step_spec: types.TimeStep, alpha: float = 1.0, emit_policy_info: Sequence[Text] = (), emit_log_probability: bool = False, accepts_per_arm_features: bool = False, distributed_use_reward_layer: bool = False, observation_and_action_constraint_splitter: Optional[ types.Splitter] = None, name: Optional[Text] = None): """Initializes `NeuralLinUCBPolicy`. Args: encoding_network: network that encodes the observations. encoding_dim: (int) dimension of the encoded observations. reward_layer: final layer that predicts the expected reward per arm. In case the policy accepts per-arm features, the output of this layer has to be a scalar. This is because in the per-arm case, all encoded observations have to go through the same computation to get the reward estimates. The `num_actions` dimension of the encoded observation is treated as a batch dimension in the reward layer. epsilon_greedy: (float) representing the probability of choosing a random action instead of the greedy action. actions_from_reward_layer: (boolean variable) whether to get actions from the reward layer or from LinUCB. cov_matrix: list of the covariance matrices. There exists one covariance matrix per arm, unless the policy accepts per-arm features, in which case this list must have a single element. data_vector: list of the data vectors. A data vector is a weighted sum of the observations, where the weight is the corresponding reward. Each arm has its own data vector, unless the policy accepts per-arm features, in which case this list must have a single element. num_samples: list of number of samples per arm. If the policy accepts per- arm features, this is a single-element list counting the number of steps. time_step_spec: A `TimeStep` spec of the expected time_steps. alpha: (float) non-negative weight multiplying the confidence intervals. emit_policy_info: (tuple of strings) what side information we want to get as part of the policy info. Allowed values can be found in `policy_utilities.PolicyInfo`. emit_log_probability: (bool) whether to emit log probabilities. accepts_per_arm_features: (bool) Whether the policy accepts per-arm features. distributed_use_reward_layer: (bool) Whether to pick the actions using the network or use LinUCB. This applies only in distributed training setting and has a similar role to the `actions_from_reward_layer` mentioned above. observation_and_action_constraint_splitter: A function used for masking valid/invalid actions with each state of the environment. The function takes in a full observation and returns a tuple consisting of 1) the part of the observation intended as input to the bandit policy and 2) the mask. The mask should be a 0-1 `Tensor` of shape `[batch_size, num_actions]`. This function should also work with a `TensorSpec` as input, and should output `TensorSpec` objects for the observation and mask. name: The name of this policy. """ policy_utilities.check_no_mask_with_arm_features( accepts_per_arm_features, observation_and_action_constraint_splitter) encoding_network.create_variables() self._encoding_network = encoding_network self._reward_layer = reward_layer self._encoding_dim = encoding_dim if accepts_per_arm_features and reward_layer.units != 1: raise ValueError( 'The output dimension of the reward layer must be 1, got' ' {}'.format(reward_layer.units)) if not isinstance(cov_matrix, (list, tuple)): raise ValueError( 'cov_matrix must be a list of matrices (Tensors).') self._cov_matrix = cov_matrix if not isinstance(data_vector, (list, tuple)): raise ValueError( 'data_vector must be a list of vectors (Tensors).') self._data_vector = data_vector if not isinstance(num_samples, (list, tuple)): raise ValueError( 'num_samples must be a list of vectors (Tensors).') self._num_samples = num_samples self._alpha = alpha self._actions_from_reward_layer = actions_from_reward_layer self._epsilon_greedy = epsilon_greedy self._dtype = self._data_vector[0].dtype self._distributed_use_reward_layer = distributed_use_reward_layer if len(cov_matrix) != len(data_vector): raise ValueError( 'The size of list cov_matrix must match the size of ' 'list data_vector. Got {} for cov_matrix and {} ' 'for data_vector'.format(len(self._cov_matrix), len((data_vector)))) if len(num_samples) != len(cov_matrix): raise ValueError('The size of num_samples must match the size of ' 'list cov_matrix. Got {} for num_samples and {} ' 'for cov_matrix'.format(len(self._num_samples), len((cov_matrix)))) self._accepts_per_arm_features = accepts_per_arm_features if observation_and_action_constraint_splitter is not None: context_spec, _ = observation_and_action_constraint_splitter( time_step_spec.observation) else: context_spec = time_step_spec.observation if accepts_per_arm_features: self._num_actions = tf.nest.flatten(context_spec[ bandit_spec_utils.PER_ARM_FEATURE_KEY])[0].shape.as_list()[0] self._num_models = 1 else: self._num_actions = len(cov_matrix) self._num_models = self._num_actions cov_matrix_dim = tf.compat.dimension_value(cov_matrix[0].shape[0]) if self._encoding_dim != cov_matrix_dim: raise ValueError('The dimension of matrix `cov_matrix` must match ' 'encoding dimension {}.' 'Got {} for `cov_matrix`.'.format( self._encoding_dim, cov_matrix_dim)) data_vector_dim = tf.compat.dimension_value(data_vector[0].shape[0]) if self._encoding_dim != data_vector_dim: raise ValueError( 'The dimension of vector `data_vector` must match ' 'encoding dimension {}. ' 'Got {} for `data_vector`.'.format(self._encoding_dim, data_vector_dim)) action_spec = tensor_spec.BoundedTensorSpec(shape=(), dtype=tf.int32, minimum=0, maximum=self._num_actions - 1, name='action') self._emit_policy_info = emit_policy_info predicted_rewards_mean = () if policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN in emit_policy_info: predicted_rewards_mean = tensor_spec.TensorSpec( [self._num_actions], dtype=tf.float32) predicted_rewards_optimistic = () if (policy_utilities.InfoFields.PREDICTED_REWARDS_OPTIMISTIC in emit_policy_info): predicted_rewards_optimistic = tensor_spec.TensorSpec( [self._num_actions], dtype=tf.float32) if accepts_per_arm_features: chosen_arm_features_info_spec = ( policy_utilities.create_chosen_arm_features_info_spec( time_step_spec.observation)) info_spec = policy_utilities.PerArmPolicyInfo( predicted_rewards_mean=predicted_rewards_mean, predicted_rewards_optimistic=predicted_rewards_optimistic, chosen_arm_features=chosen_arm_features_info_spec) else: info_spec = policy_utilities.PolicyInfo( predicted_rewards_mean=predicted_rewards_mean, predicted_rewards_optimistic=predicted_rewards_optimistic) super(NeuralLinUCBPolicy, self).__init__(time_step_spec=time_step_spec, action_spec=action_spec, emit_log_probability=emit_log_probability, observation_and_action_constraint_splitter=( observation_and_action_constraint_splitter), info_spec=info_spec, name=name)