Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning
Figure 1
Euclidean spatial generalization benefits learning in simple navigation tasks.
(A) Each column displays the gridworld configuration whereby individual squares are discrete states, thick black lines are walls, and the star indicates the goal state with reward of 1. (B) Each column shows performance measured as the mean number of steps to goal over 10,000 runs for the environment in the corresponding column in A. The width of each line occupies at least the 95% confidence intervals on the means (range 3.9–4.4 steps). Within a given gridworld the different colored lines represent different basis sets with black for tabular, blue for grid cells, and red for place cells.