  Language models have demonstrated impressive capabilities across various
natural language processing tasks, yet they struggle with planning tasks
requiring multi-step simulations. Inspired by human cognitive processes, this
paper investigates the optimal planning power of language models that can
construct a cognitive map of a given environment. Our experiments demonstrate
that cognitive map significantly enhances the performance of both optimal and
reachable planning generation ability in the Gridworld path planning task. We
observe that our method showcases two key characteristics similar to human
cognition: \textbf{generalization of its planning ability to extrapolated
environments and rapid adaptation with limited training data.} We hope our
findings in the Gridworld task provide insights into modeling human cognitive
processes in language models, potentially leading to the development of more
advanced and robust systems that better resemble human cognition.
