f52db36a739dfd7385c7bb5c60593debbbc3a8b1,tensorforce/models/naf_model.py,NAFModel,update,#NAFModel#Any#,106

Before Change

        q_targets = batch["rewards"] + (1. - float_terminals) * self.gamma * np.squeeze(
            self.get_target_value_estimate(batch["next_states"]))

        self.session.run([self.optimize_op, self.loss, self.training_v, self.advantage, self.q], {
            self.q_targets: q_targets,
            self.actions: batch["actions"],
            self.state: batch["states"]})

    def create_outputs(self, last_hidden_layer, scope):
        
        Creates NAF specific outputs.

After Change

        q_targets = batch["rewards"] + (1. - float_terminals) * self.gamma * np.squeeze(
            self.get_target_value_estimate(batch["next_states"]))

        feed_dict = {
            self.episode_length: [len(batch["rewards"])],
            self.q_targets: q_targets,
            self.actions: batch["actions"],
            self.state: batch["states"]}

        fetches = [self.optimize_op, self.loss, self.training_v, self.advantage, self.q]
        fetches.extend(self.training_network.internal_state_outputs)
        fetches.extend(self.target_network.internal_state_outputs)

        for n, internal_state in enumerate(self.training_network.internal_state_inputs):
            feed_dict[internal_state] = self.training_internal_states[n]

        for n, internal_state in enumerate(self.target_network.internal_state_inputs):
            feed_dict[internal_state] = self.target_internal_states[n]

        fetched = self.session.run(fetches, feed_dict)

        self.training_internal_states = fetched[2:len(self.training_internal_states)]
        self.target_internal_states = fetched[2 + len(self.training_internal_states):]

    def create_outputs(self, last_hidden_layer, scope):
        

Instances