Reinforcement Learning

  1. Mathematical Foundations
    1. Probability Theory Essentials
      1. Random Variables
        1. Probability Distributions
          1. Expectation and Variance
            1. Conditional Probability
            2. Optimization Fundamentals
              1. Gradient Descent
                1. Stochastic Optimization
                  1. Convex vs Non-convex Problems
                  2. Linear Algebra Basics
                    1. Vectors and Matrices
                      1. Matrix Operations
                        1. Eigenvalues and Eigenvectors

                      Previous

                      1. Foundations of Reinforcement Learning

                      Go to top

                      Next

                      3. Markov Decision Processes