models/baseline_model.py
def forward(self, input_dict, state, seq_lens):
        """
        Evaluate the model.
        Adds time dimension to batch before sending inputs to forward_rnn()
        :param input_dict: The input tensors.
        :param state: The model state.
        :param seq_lens: LSTM sequence lengths.
        :return: The policy logits and state.
        """
        trunk = self.encoder_model(input_dict["obs"]["curr_obs"])
        new_dict = {"curr_obs": add_time_dimension(trunk, seq_lens)}

        output, new_state = self.forward_rnn(new_dict, state, seq_lens)
        return tf.reshape(output, [-1, self.num_outputs]), new_state

        Evaluate the model.
        Adds time dimension to batch before sending inputs to forward_rnn()
        :param input_dict: The input tensors.
        :param state: The model state.
        :param seq_lens: LSTM sequence lengths.
        :return: The policy logits and state.
        
Evaluate the model.
Adds time dimension to batch before sending inputs to forward_rnn()
Evaluate the model . Adds time dimension to batch before sending inputs to forward_rnn ()
def forward(self, input_dict, state, seq_lens):
        trunk = self.encoder_model(input_dict["obs"]["curr_obs"])
        new_dict = {"curr_obs": add_time_dimension(trunk, seq_lens)}
        output, new_state = self.forward_rnn(new_dict, state, seq_lens)
        return tf.reshape(output, [-1, self.num_outputs]), new_state
def forward ( self , input_dict , state , seq_lens ) : trunk = self . encoder_model ( input_dict [ "obs" ] [ "curr_obs" ] ) new_dict = { "curr_obs" : add_time_dimension ( trunk , seq_lens ) } output , new_state = self . forward_rnn ( new_dict , state , seq_lens ) return tf . reshape ( output , [ - 1 , self . num_outputs ] ) , new_state
Evaluate the model.
Evaluate the model .
"""
        Evaluate the model.
        Adds time dimension to batch before sending inputs to forward_rnn()
        :param input_dict: The input tensors.
        :param state: The model state.
        :param seq_lens: LSTM sequence lengths.
        :return: The policy logits and state.
        """
The policy logits and state.
The policy logits and state .
self
input_dict
The input tensors.
The input tensors .
state
The model state.
The model state .
seq_lens
LSTM sequence lengths.
LSTM sequence lengths .