Machine learning with rewards (as opposed to supervised or unsupervised methods).

Page List

Definitions & Concepts — Concise list of terms and concepts in reinforcement learning.
Dynamic Programming — Markov decision processes formally describe an environment for Reinforcement Learning when the environment is fully observable.
Fundamentals — Introduction to the fundamentals of reinforcement learning.
Markov Decision Processes — Markov decision processes formally describe an environment for Reinforcement Learning when the environment is fully observable.
Model Free — Markov decision processes formally describe an environment for Reinforcement Learning when the environment is not observable.
Value Function Approximation — Approximating the state or action value function.

Resources

Metric Learning
- contributed Apr 17, 2018
Maximum Likelihood, Maximum A Posteriori and Bayesian
- contributed Apr 10, 2018
Value Function Approximation
- contributed Feb 22, 2018
Definitions & Concepts
- contributed Feb 22, 2018
Model Free
- contributed Feb 11, 2018