  The environments where individuals live can present diverse navigation
challenges, resulting in varying navigation abilities and strategies. Inspired
by differing urban layouts and the Dual Solutions Paradigm test used for human
navigators, we developed a simulated navigation environment to train deep
reinforcement learning agents in a shortcut usage task. We modulated the
frequency of exposure to a shortcut and navigation cue, leading to the
development of artificial agents with differing abilities. We examined the
encoded representations in artificial neural networks driving these agents,
revealing intricate dynamics in representation learning, and correlated them
with shortcut use preferences. Furthermore, we demonstrated methods to analyze
representations across a population of nodes, which proved effective in finding
patterns in what would otherwise be noisy single-node data. These techniques
may also have broader applications in studying neural activity. From our
observations in representation learning dynamics, we propose insights for human
navigation learning, emphasizing the importance of navigation challenges in
developing strong landmark knowledge over repeated exposures to landmarks
alone.
