A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation
The study investigated how adding a nonlinear hidden layer to actor-critic agents improves their ability to learn multiple paired association navigation tasks, such as learning where two rewards are located based on cues. The findings show that a nonlinear hidden layer agent significantly outperforms classic and linear agents in learning these complex associations, achieving above-chance performance.