Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation
Fig 6
Comparison of policy entropy and sensitivity to learning rate among agents with symmetric and asymmetric learning rule.
Left: Policy entropy of the two agents show different trajectories in the generalization experiment. We calculated the entropy of the agent’s policy, averaged over all states, at the end of each episode. This reveals that during learning to navigate to the first target, the symmetric agent has more entropy, which is then reversed when the new target has to be reached. Right: Symmetric agent shows more sensitivity to learning rate parameter for lower learning rates. We trained the agents repeatedly until a fixed accuracy in navigation to the target was met. We then recorded the number of episodes it took until that criterion was reached. Curves show median and interquartile range of this number for the two agents.