Reinforcement Learning slides 2024 Outline The 2024 course consists of the following topics Lecture 01 Introduction Lecture 02 MDPs; value and Q-functions; value iteration, policy iteration; operator perspectives. Model-free policy-based and value-based methods; connections to gradient methods; Monte Carlo (MC) method and temporal difference (TD) learning. Lecture 03 Primal and Dual LP, primal-dual methods, REPS.r algebra reminder Lecture 04 Policy parameterizations, policy gradient theorems and estimators, performance difference lemma, gradient dominance and convergence of policy gradient methods, narual policy gradient Lecture 05 NPG, sample-based NPG, TRPO, exploration in policy gradients Lecture 06 Behavioral cloning, dagger, MCE-IRL, GAIL, P2IL, IQ-Learn Lecture 07 NFG, equilibria, response dynamics of iterated play, Markov games, RL dynamics in Markov games Lecture 08 Actor Critic based Deep RL: TRPO, Soft Actor Critic.Value based Deep RL: DQN, Double DQN, Rainbow.Robust RL and IRL.
