We present the first mechanistic evidence that model-free reinforcement
learning agents can learn to plan. This is achieved by applying a methodology
based on concept-based interpretability to a model-free agent in Sokoban — a
commonly used benchmark for studying planning. Specifically, we demonstrate
that DRC, a generic model-free agent introduced by Guez et al. (2019), uses
learned concept representations to internally formulate plans that both predict
the long-term effects of actions on the environment and influence action
selection. Our methodology involves: (1) probing for planning-relevant
concepts, (2) investigating plan formation within the agent’s representations,
and (3) verifying that discovered plans (in the agent’s representations) have a
causal effect on the agent’s behavior through interventions. We also show that
the emergence of these plans coincides with the emergence of a planning-like
property: the ability to benefit from additional test-time compute. Finally, we
perform a qualitative analysis of the planning algorithm learned by the agent
and discover a strong resemblance to parallelized bidirectional search. Our
findings advance understanding of the internal mechanisms underlying planning
behavior in agents, which is important given the recent trend of emergent
planning and reasoning capabilities in LLMs through RL