Linear Recurrent Memory Suffices to Distil a World-Model Policy for Robot Air Hockey

Excerpt

arXiv:2609.39151v1 Announce Type: cross Abstract: Does memory-dependent control need nonlinear recurrent dynamics? We study simulated air-hockey defence under temporary loss of puck tracking. A DreamerV3 teacher outperforms a memoryless policy under tracking loss, while resetting the teacher's recurrent state sharply reduces performance, which demonstrates that the task requires memory. We distil this teacher into compact recurrent policies with a 64 dimensional state, with a combination of a di