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AI model for Sokoban game uses 'path channels' for planning

By PulseAugur Editorial · [1 sources] ·

Researchers have partially reverse-engineered a convolutional recurrent neural network (RNN) used for the game Sokoban. They discovered that the network stores future moves, or plans, as activations within specific "path channels" in its hidden state. These channels are influenced by convolutional kernels that encode learned transition models, allowing the RNN to construct plans by propagating activations from boxes to goals and using negative values to prune paths at obstacles, effectively implementing a form of backtracking. AI

RANK_REASON The cluster contains an academic paper detailing a novel mechanistic description of planning in a trained neural network. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model for Sokoban game uses 'path channels' for planning

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The cluster contains an academic paper detailing a novel mechanistic description of planning in a trained neural network. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mohammad Taufeeque, Aaron David Tucker, Adam Gleave, Adri\`a Garriga-Alonso ·

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

    arXiv:2506.10138v3 Announce Type: replace-cross 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 activati…

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