1955745230c9f267613ebae9c4febd6fdcaa08cf,ml/rl/training/continuous_action_dqn_predictor.py,ContinuousActionDQNPredictor,predict,#ContinuousActionDQNPredictor#Any#Any#,22

Before Change

        previous_workspace = workspace.CurrentWorkspace()
        workspace.SwitchWorkspace(self._workspace_id)
        for name, value in states.items():
            workspace.FeedBlob(name, np.atleast_1d(value).astype(np.float32))
        for name, value in actions.items():
            workspace.FeedBlob(name, np.atleast_1d(value).astype(np.float32))
        workspace.RunNet(self._net)
        result = {"Q": workspace.FetchBlob(self._output_blobs[0])}

After Change

        

        examples = []
        for i in range(len(states)):
            examples.append({**states[i], **actions[i]})
        return RLPredictor.predict(self, examples)

    def get_predictor_export_meta(self):
        return PredictorExportMeta(

Instances