Q-learning

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

Q-learning - Wikipedia Jump to content From Wikipedia, the free encyclopedia Model-free reinforcement learning algorithm 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 Online learning Batch learning Curriculum learning Rule-based learning Neuro-symbolic AI Neuromorphic engineering Quantum machine learning Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML

related reading

• A (Long) Peek into Reinforcement Learning | Lil'Loglilianweng.github.io
• Part 1: Key Concepts in RL - Spinning Up documentationspinningup.openai.com
• Deep Q-Networks Explained — LessWronglesswrong.com
• Part 2: Kinds of RL Algorithms - Spinning Up documentationspinningup.openai.com
• Technical Note: Q-Learning | Machine Learning | Springer Nature Linklink.springer.com
• Q-learning is not yet scalableseohong.me
• An Updated Introduction to Reinforcement Learning | Sri's Blogsrianumakonda.com
• Reinforcement learning - Wikipediaen.wikipedia.org
• Model-free (reinforcement learning) - Wikipediaen.wikipedia.org
• Learn Reinforcement Learning (2) - DQN · greentec's bloggreentec.github.io
• Epsilon-Greedy Q-learning | Baeldung on Computer Sciencebaeldung.com
• Reinforcement Learning in Newcomblike Problemsproceedings.neurips.cc