Machine learningDeep learning / NLP / CV
Reinforcement Learning
Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback.
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Sources
- Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
- Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533. DOI: 10.1038/nature14236 ↗
Related methods
Referenced by
Agent-based dynamic programmingBayesian Dynamic ProgrammingExplainable Reinforcement LearningFine-Tuned Reinforcement LearningMultilingual Reinforcement LearningMultimodal Reinforcement LearningSelf-supervised Reinforcement LearningSemi-supervised Reinforcement LearningTransfer Learning with Reinforcement LearningWeakly supervised reinforcement learning