Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Reinforcement learning - Wikipedia Jump to content From Wikipedia, the free encyclopedia Field of machine learning For reinforcement learning in psychology, see Reinforcement and Operant conditioning . 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. Part of a series on Machine learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning Meta-learning Onlin

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• A (Long) Peek into Reinforcement Learning | Lil'Loglilianweng.github.io
• Part 1: Key Concepts in RL - Spinning Up documentationspinningup.openai.com
• Reward is not the optimization target — LessWronglesswrong.com
• SuttonBartoIPRLBook2ndEd.pdfweb.stanford.edu
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