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Navigation to multiple cued reward locations has been increasingly used tostudy rodent learning. Though deep reinforcement learning agents have beenshown to be able to learn the task, they are not biologically plausible.Biologically plausible classic actor-critic agents have been shown to learn tonavigate to single reward locations, but which biologically plausible agentsare able to learn multiple cue-reward location tasks has remained unclear. Inthis computational study, we show versions of classic agents that learn tonavigate to a single reward location, and adapt to reward locationdisplacement, but are not able to learn multiple paired association navigation.The limitation is overcome by an agent in which place cell and cue informationare first processed by a feedforward nonlinear hidden layer with synapses tothe actor and critic subject to temporal difference error-modulated plasticity.Faster learning is obtained when the feedforward layer is replaced by arecurrent reservoir network.

A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation