models/baseline_model.py
def forward_rnn(self, input_dict, state, seq_lens):
        """
        Forward pass through the LSTM.
        Implicitly assigns the value function output to self_value_out, and does not return this.
        :param input_dict: The input tensors.
        :param state: The model state.
        :param seq_lens: LSTM sequence lengths.
        :return: The policy logits and new state.
        """
        h1, c1 = state

        # Compute the next action
        (self._model_out, self._value_out, output_h1, output_c1,) = self.policy_model.forward_rnn(
            input_dict, [h1, c1], seq_lens
        )

        return self._model_out, [output_h1, output_c1]

        Forward pass through the LSTM.
        Implicitly assigns the value function output to self_value_out, and does not return this.
        :param input_dict: The input tensors.
        :param state: The model state.
        :param seq_lens: LSTM sequence lengths.
        :return: The policy logits and new state.
        
Forward pass through the LSTM.
Implicitly assigns the value function output to self_value_out, and does not return this.
Forward pass through the LSTM . Implicitly assigns the value function output to self_value_out and does not return this .
def forward_rnn(self, input_dict, state, seq_lens):
        h1, c1 = state
        (self._model_out, self._value_out, output_h1, output_c1,) = self.policy_model.forward_rnn(
            input_dict, [h1, c1], seq_lens
        )
        return self._model_out, [output_h1, output_c1]
def forward_rnn ( self , input_dict , state , seq_lens ) : h1 , c1 = state ( self . _model_out , self . _value_out , output_h1 , output_c1 , ) = self . policy_model . forward_rnn ( input_dict , [ h1 , c1 ] , seq_lens ) return self . _model_out , [ output_h1 , output_c1 ]
Forward pass through the LSTM.
Forward pass through the LSTM .
"""
        Forward pass through the LSTM.
        Implicitly assigns the value function output to self_value_out, and does not return this.
        :param input_dict: The input tensors.
        :param state: The model state.
        :param seq_lens: LSTM sequence lengths.
        :return: The policy logits and new state.
        """ # Compute the next action
The policy logits and new state.
The policy logits and new 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 .