Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation
Fig 5
Symmetric successor representation agent affords better generalization in simple navigation tasks.
Left: Agents started in random locations in the environments and had to learn to navigate to fixed targets. After 400 episodes, reward location was switched to a new random location, where agents could only relearn the reward prediction vector but not the SR. (Generalization) performance is visualized by total number of steps taken per episode, for an agent using the classical rule (blue) and an agent using the symmetric rule (red). Dashed line indicates change of target location. We show the average performance over different environments as performance is qualitatively similar, see S3 Fig for plots in individual environments. Right: Similar to left plot, but instead of switching target after a fixed number of episodes, the target was switched when the previous target was found with a fixed accuracy. Violin plots show distribution of suboptimality (steps - optimal number of steps) over all environments, for individual environments see S4 Fig. For an outline of the environments see S2 Fig.