Lecture 1 - Introduction, Elements of RL, MDP, Bellman Equation.
Lecture 2 - Policy evaluation, Policy improvement, Policy iteration.
Lecture 3 - Value iteration, Monte Carlo methods.
Lecture 4 - Value Temporal difference learning, Eligibility traces.
Lecture 5 - Model free control
Lecture 7 - Value Function Approximation
Lecture 8 - Policy gradient, Policy gradient theorem, Reinforce algorithm