Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning
This toolkit addresses the need for reproducible benchmarks in neurally plausible reinforcement learning, benefiting cognitive scientists and AI researchers, but it is incremental as it standardizes existing methods rather than introducing new ones.
The authors introduced Neuro-Nav, an open-source library that provides standardized environments and reinforcement learning algorithms for neurally plausible research, and demonstrated its ability to replicate findings from cognitive science and RL studies.
In this work we propose Neuro-Nav, an open-source library for neurally plausible reinforcement learning (RL). RL is among the most common modeling frameworks for studying decision making, learning, and navigation in biological organisms. In utilizing RL, cognitive scientists often handcraft environments and agents to meet the needs of their particular studies. On the other hand, artificial intelligence researchers often struggle to find benchmarks for neurally and biologically plausible representation and behavior (e.g., in decision making or navigation). In order to streamline this process across both fields with transparency and reproducibility, Neuro-Nav offers a set of standardized environments and RL algorithms drawn from canonical behavioral and neural studies in rodents and humans. We demonstrate that the toolkit replicates relevant findings from a number of studies across both cognitive science and RL literatures. We furthermore describe ways in which the library can be extended with novel algorithms (including deep RL) and environments to address future research needs of the field.