Path Channels and Plan Extension Kernels: a Mechanistic Description of Planning in a Sokoban RNN

arXiv:2506.10138v3 Announce Type: replace Abstract: We partially reverse-engineer a convolutional recurrent neural network (RNN) trained with model-free reinforcement learning to play the box-pushing game Sokoban. We find that the RNN stores future moves (plans) as activations in particular channels of the hidden state, which we call path channels. A high activation in a particular location means that, when a box is in that location, it will get pushed in the channel's assigned direction. We examine the convolutional kernels between path channels and find that they encode the change in positio

Source: arXiv cs.LG — read the full report at the original publisher.

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