reinforcement learning

Also known as RL

type of machine learning where an agent learns how to behave in an environment by performing actions and receiving rewards or penalties in return, aiming to maximize the cumulative reward over time

Research

89,335 papers

Actor-critic reinforcement learning in the songbird.Current opinion in neurobiology · 2020
Pavlovian learning and conditioned reinforcement.Journal of applied behavior analysis · 2023
The functions of the orbitofrontal cortex.Brain and cognition · 2004
Deep Reinforcement Learning With Modulated Hebbian Plus Q-Network Architecture.IEEE transactions on neural networks and learning systems · 2022
Learning with reinforcement prediction errors in a model of the Drosophila mushroom body.Nature communications · 2021

via PubMed

Article

The typical framing of a reinforcement learning (RL) scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent.

In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning.