Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation
Fig 9
Generalization performance in maze task with blocked paths
Top: Grid world mazes used for generalization task. Leftmost maze was used for training, the other three environments for testing the generalization. Bottom: Violin plots show distribution of suboptimality (steps - optimal number of steps) of the agents when using the successor representation trained on one target and tested on another one. Training and test targets are randomly drawn from all possible states in the respective environments. The distribution for the symmetric agent is broader around 0, which indicates optimal generalization, and less pronounced at 400, which was the maximum number of steps allowed.