Interpreting Emergent Planning in Model-Free Reinforcement Learning
Concept-based analysis of a Sokoban agent finds internal representations that predict long-term action effects, causally guide behavior, and function like plans.
Researchers applied concept-based interpretability methods to a model-free reinforcement-learning agent solving Sokoban puzzles. They identified internal representations that predict the long-term effects of actions and causally influence what the agent does.
The representations behave like plans and resemble a parallel, bidirectional search process. The result offers mechanistic evidence that planning-like computation can emerge inside a model-free agent.
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