magehrke/mlc

Machine Learning Collection

Machine Learning Collection

Basic Reinforcement Learning:

Dynamic Programming:

• Value Iteration
• Policy Iteration

Temporal-Difference Learning:

• Q-Learning
• SARSA

Policy Gradient Methods:

• REINFORCE

Reference: Reinforcement Learning: An Introduction by Sutton and Barto.

Advanced Reinforcement Learning:

• DQN
• DDPG
• LSPI
• REPS
• PPO
• TRPO
• PILCO
• NAC

Nonparametric Methods:

• Histograms
• Kernel Density Estimators
• Nearest Neighbours

Reference: Pattern Recognition And Machine Learning by Bishop.

Mixture Models and EM:

• K-means Clustering
• Mixtures of Gaussians
• Alternative views of EM

Reference: Pattern Recognition And Machine Learning by Bishop.

Others / not categorized yet:

• Gaussian Processes
• Support Vector Machines
• Bayesian Optimization
• LQR
• Mixed Integer Programming
• OptNet

mlc's Issues

Organizing k-means clustering

k-means clustering needs:

• better comments
• a clear structure
• Readme file which includes needed packages