Source code for ray.rllib.models.tf.recurrent_tf_modelv2

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.policy.rnn_sequencing import add_time_dimension
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils import try_import_tf

tf = try_import_tf()


[docs]@DeveloperAPI class RecurrentTFModelV2(TFModelV2): """Helper class to simplify implementing RNN models with TFModelV2. Instead of implementing forward(), you can implement forward_rnn() which takes batches with the time dimension added already."""
[docs] def __init__(self, obs_space, action_space, num_outputs, model_config, name): """Initialize a TFModelV2. Here is an example implementation for a subclass ``MyRNNClass(RecurrentTFModelV2)``:: def __init__(self, *args, **kwargs): super(MyModelClass, self).__init__(*args, **kwargs) cell_size = 256 # Define input layers input_layer = tf.keras.layers.Input( shape=(None, obs_space.shape[0])) state_in_h = tf.keras.layers.Input(shape=(256, )) state_in_c = tf.keras.layers.Input(shape=(256, )) seq_in = tf.keras.layers.Input(shape=(), dtype=tf.int32) # Send to LSTM cell lstm_out, state_h, state_c = tf.keras.layers.LSTM( cell_size, return_sequences=True, return_state=True, name="lstm")( inputs=input_layer, mask=tf.sequence_mask(seq_in), initial_state=[state_in_h, state_in_c]) output_layer = tf.keras.layers.Dense(...)(lstm_out) # Create the RNN model self.rnn_model = tf.keras.Model( inputs=[input_layer, seq_in, state_in_h, state_in_c], outputs=[output_layer, state_h, state_c]) self.register_variables(self.rnn_model.variables) self.rnn_model.summary() """ TFModelV2.__init__(self, obs_space, action_space, num_outputs, model_config, name)
@override(ModelV2) def forward(self, input_dict, state, seq_lens): """Adds time dimension to batch before sending inputs to forward_rnn(). You should implement forward_rnn() in your subclass.""" output, new_state = self.forward_rnn( add_time_dimension(input_dict["obs_flat"], seq_lens), state, seq_lens) return tf.reshape(output, [-1, self.num_outputs]), new_state
[docs] def forward_rnn(self, inputs, state, seq_lens): """Call the model with the given input tensors and state. Arguments: inputs (dict): observation tensor with shape [B, T, obs_size]. state (list): list of state tensors, each with shape [B, T, size]. seq_lens (Tensor): 1d tensor holding input sequence lengths. Returns: (outputs, new_state): The model output tensor of shape [B, T, num_outputs] and the list of new state tensors each with shape [B, size]. Sample implementation for the ``MyRNNClass`` example:: def forward_rnn(self, inputs, state, seq_lens): model_out, h, c = self.rnn_model([inputs, seq_lens] + state) return model_out, [h, c] """ raise NotImplementedError("You must implement this for a RNN model")
[docs] def get_initial_state(self): """Get the initial recurrent state values for the model. Returns: list of np.array objects, if any Sample implementation for the ``MyRNNClass`` example:: def get_initial_state(self): return [ np.zeros(self.cell_size, np.float32), np.zeros(self.cell_size, np.float32), ] """ raise NotImplementedError("You must implement this for a RNN model")