Reinforcement Learning
Random Variables
Probability Distributions
Expectation and Variance
Conditional Probability
Gradient Descent
Stochastic Optimization
Convex vs Non-convex Problems
Vectors and Matrices
Matrix Operations
Eigenvalues and Eigenvectors
Previous
1. Foundations of Reinforcement Learning
Go to top
Next
3. Markov Decision Processes