Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex ways, forming recurrent neural networks, and learning modifies the strength of their connections. Moreover, neurons communicate emitting brief discrete electric signals. Here we describe how to train recurrent neural networks in tasks like those used to train animals in neuroscience laboratories, and how computations emerge in the trained networks. Surprisingly, artificial networks and real brains can use similar computational strategies.

SKRL是一个开源模块化库，用于用Python编写的加固学习，设计着专注于算法实现的可读性，简单性和透明度。除了使用OpenAi Gym和DeepMind的传统接口的支持环境外，它还提供了装载，配置和操作NVIDIA ISAAC健身房和Nvidia Omniverse Isaac Gym Gym Gunt环境的设施。此外，它可以同时对几个具有可定制范围的代理（所有可用环境的子集）进行培训，这些代理在同一运行中可能会或可能不会共享资源。可以在https://skrl.readthedocs.io上找到该库的文档，其源代码可在https://github.com/toni-sm/skrl上找到。