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
This paper represents a significant step in mechanistic interpretability, a growing field aiming to understand the inner workings of AI models, which is crucial as AI systems become more complex and autonomous.
Understanding how AI models plan and reason at a fundamental level is essential for developing more reliable, controllable, and robust AI agents, enabling their deployment in sensitive applications.
The ability to reverse-engineer planning mechanisms in an RNN provides a blueprint for scrutinizing other complex neural networks, moving beyond black-box observations to explainable AI.
- · AI safety researchers
- · Autonomous systems developers
- · Interpretability software vendors
- · Developers of uninterpretable AI models
- · Domains requiring high trust without transparency
This research provides a methodology to deconstruct the 'planning' capabilities within certain AI models.
Improved understanding could lead to the design of more efficient and less 'black box' AI architectures specifically for planning tasks.
The ability to reliably understand and verify AI planning could accelerate the deployment of AI agents in mission-critical applications, such as infrastructure management or defense.
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