Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences.

ABSTRACT: Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing the number of necessary interactions with the environment to learn a desirable policy. However, these methods require biological implausible ingredients, such as the detailed storage of older experiences, and long periods of offline learning. The optimal way to learn and exploit world-models is still an open question. Taking inspiration from biology, we suggest that dreaming might be an efficient expedient to use an inner model. We propose a two-module (agent and model) spiking neural network in which "dreaming" (living new experiences in a model

SUBMITTER: Capone C 

PROVIDER: S-EPMC11199658 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature