LEC 24: Fitted Value Iteration_Theory and Practical Applications in Reinforcement Learning
Channel: Abhishek Gupta
Date: 2020-04-11

In this 24th lecture of ECE 8851: Reinforcement Learning, we focus on Fitted Value Iteration, a powerful algorithm for solving Markov Decision Processes with a generative model. Fitted Value Iteration is a type of dynamic programming algorithm that uses a function approximator to estimate the value function of an environment.

During the lecture, we discuss the inherent Bellman error of a function approximating class and the theoretical convergence guarantee of fitted value iteration. We also explore how Fitted Value Iteration can be used to solve complex problems, such as those with high-dimensional state spaces.