garage.tf.q_functions.discrete_mlp_q_function
¶
Discrete MLP QFunction.
-
class
DiscreteMLPQFunction
(env_spec, name=None, hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), layer_normalization=False)¶ Bases:
garage.tf.models.MLPModel
Discrete MLP Q Function.
This class implements a Q-value network. It predicts Q-value based on the input state and action. It uses an MLP to fit the function Q(s, a).
Parameters: - env_spec (EnvSpec) – Environment specification.
- name (str) – Name of the q-function, also serves as the variable scope.
- hidden_sizes (list[int]) – Output dimension of dense layer(s). For example, (32, 32) means the MLP of this q-function consists of two hidden layers, each with 32 hidden units.
- hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
- hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
- hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
- output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
- output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.
- output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
- layer_normalization (bool) – Bool for using layer normalization.
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q_vals
¶ Return the Q values, the output of the network.
Returns: Q values. Return type: list[tf.Tensor]
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input
¶ Get input.
Returns: QFunction Input. Return type: tf.Tensor
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parameters
¶ Parameters of the model.
Returns: Parameters Return type: np.ndarray
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name
¶ Name (str) of the model.
This is also the variable scope of the model.
Returns: Name of the model. Return type: str
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output
¶ Default output of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the output of the network.
Returns: Default output of the model. Return type: tf.Tensor
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inputs
¶ Default inputs of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the inputs of the network.
Returns: Default inputs of the model. Return type: list[tf.Tensor]
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outputs
¶ Default outputs of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the outputs of the network.
Returns: Default outputs of the model. Return type: list[tf.Tensor]
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state_info_specs
¶ State info specification.
Returns: - keys and shapes for the information related to the
- module’s state when taking an action.
Return type: List[str]
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state_info_keys
¶ State info keys.
Returns: - keys for the information related to the module’s state
- when taking an input.
Return type: List[str]
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build
(self, state_input, name)¶ Build the symbolic graph for q-network.
Parameters: - state_input (tf.Tensor) – The state input tf.Tensor to the network.
- name (str) – Network variable scope.
Returns: The tf.Tensor output of Discrete MLP QFunction.
Return type: tf.Tensor
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clone
(self, name)¶ Return a clone of the Q-function.
It copies the configuration of the primitive and also the parameters.
Parameters: name (str) – Name of the newly created q-function. Returns: Clone of this object Return type: garage.tf.q_functions.DiscreteMLPQFunction
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network_input_spec
(self)¶ Network input spec.
Returns: List of key(str) for the network inputs. Return type: list[str]
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network_output_spec
(self)¶ Network output spec.
Returns: List of key(str) for the network outputs. Return type: list[str]
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reset
(self, do_resets=None)¶ Reset the module.
This is effective only to recurrent modules. do_resets is effective only to vectoried modules.
For a vectorized modules, do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs.
Parameters: do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
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terminate
(self)¶ Clean up operation.
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get_trainable_vars
(self)¶ Get trainable variables.
Returns: - A list of trainable variables in the current
- variable scope.
Return type: List[tf.Variable]
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get_global_vars
(self)¶ Get global variables.
Returns: - A list of global variables in the current
- variable scope.
Return type: List[tf.Variable]
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get_regularizable_vars
(self)¶ Get all network weight variables in the current scope.
Returns: - A list of network weight variables in the
- current variable scope.
Return type: List[tf.Variable]
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get_params
(self)¶ Get the trainable variables.
Returns: - A list of trainable variables in the current
- variable scope.
Return type: List[tf.Variable]
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get_param_shapes
(self)¶ Get parameter shapes.
Returns: A list of variable shapes. Return type: List[tuple]
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get_param_values
(self)¶ Get param values.
Returns: - Values of the parameters evaluated in
- the current session
Return type: np.ndarray
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set_param_values
(self, param_values)¶ Set param values.
Parameters: param_values (np.ndarray) – A numpy array of parameter values.
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flat_to_params
(self, flattened_params)¶ Unflatten tensors according to their respective shapes.
Parameters: flattened_params (np.ndarray) – A numpy array of flattened params. Returns: - A list of parameters reshaped to the
- shapes specified.
Return type: List[np.ndarray]