Neuro-Nav：一个神经似然强化学习库
Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning
摘要：
在这项工作中，我们提出了Neuro-Nav，一个开源的神经似然强化学习（RL）库。RL是研究生物有机体中决策、学习和导航的最常见的建模框架之一。在使用RL时，认知科学家经常手工制作环境和代理，以满足他们特定研究的需求。另一方面，人工智能研究人员往往很难找到神经和生物学上合理的表示和行为的基准（例如，在决策或导航中）。为了简化这两个领域的透明性和可重复性的过程，Neuro-Nav提供了一套标准化的环境和RL算法，这些算法来自啮齿动物和人类的典型行为和神经研究。我们证明了该工具包复制了认知科学和RL文献中许多研究的相关发现。我们还描述了可以使用新算法（包括深度RL）和环境扩展库的方法，以满足该领域未来的研究需求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
