The tutorial is on several new pieces of Reinforcement learning theory developed in the last 7 years. This includes:
1. Sample based analysis of RL including E3 and sparse sampling.
2. Generalization based analysis of RL including conservative policy iteration and RL-to-Classification reductions.
For each of these forms of theory, we cover the basic results and cover the weaknesses and strengths of the approach in context.