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

arXiv: https://arxiv.org/abs/2106.13541 · HTML: https://mlsanity.com/paper/2106.13541/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2021.

## Built on by (most cited first, 3 of 3)

- [Compositional Learning of Visually-Grounded Concepts Using Reinforcement](https://mlsanity.com/paper/2309.04504/) (2023)
- [Human-like compositional learning of visually-grounded concepts using synthetic environments](https://mlsanity.com/paper/2504.06618/) (2025)
- [Emergence of Spatial Representation in an Actor-Critic Agent with Hippocampus-Inspired Sequence Generator](https://mlsanity.com/paper/2510.09951/) (2025)

## Abstract (arXiv)

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.