Explore stochastic variance reduction methods for policy evaluation in reinforcement learning through this 47-minute lecture by Lihong Li from Microsoft Research. Delve into the challenges of mean squared projected Bellman error (MSPBE) and its formulation as a saddle-point problem. Examine various gradient-based approaches, including primal dual batch gradient, stochastic gradient descent, and advanced techniques like Stochastic Variance Reduced Gradient (SVRG) and SAGA. Gain insights into complexity analysis, preliminary experiments, and benchmarks using random MDPs and the Mountain Car problem. Compare these methods with previous work and understand their implications for interactive learning in reinforcement learning contexts.