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Active Learning

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Core-set Selection

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Expected Error Reduction

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Behavior Cloning

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Inverse Reinforcement Learning (IRL)

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Reinforcement Learning

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Bandit

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ε-Greedy

Multi-Armed Bandit

Thompson Sampling

Upper Confidence Bound (UCB)

Deep Reinforcement Learning

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Deep Q-Network (DQN)

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Q-Learning

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Self-supervised Learning

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自回归预测

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BERT-style Pretraining

Masked Autoencoder (MAE)

Masked Image Modeling

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预测式表示学习

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Semi-supervised Learning

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Transformer Classifier

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Gaussian Process Regression

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Linear Regression

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Random Forest Regression

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Support Vector Regression (SVR)

Unsupervised Learning

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聚类方法

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Gaussian Mixture Model (GMM；常用 EM 算法训练)

K-Means

Spectral Clustering

密度估计

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Gaussian Mixture Model (GMM)

Kernel Density Estimation (KDE)

降维方法

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Principal Component Analysis (PCA)

异常检测

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Environment Model

本页用于记录 Environment Model 的定义、基本思想、适用场景和相关链接。

所属路径

Reinforcement Learning / 关键对象 / Environment Model

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