Recurrent memory no longer needs training: frozen networks recall over 256 steps with half the trainable parameters
In partially observable reinforcement learning, freezing the recurrent memory module and training only the output and mixer layers still matches fully trained counterparts on delayed recall, discrete and continuous control, and pixel-based tasks, while cutting trainable parameters by more than half.