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