Reinforcement Learning for Complex Networks

This course is a graduate level course focusing on the theory and practice of reinforcement learning. Reinforcement learning is a paradigm that focuses on the question: How to interact with an environment when the decision maker's current action affects future consequences. This course provides an accessible in-depth treatment of reinforcement learning and dynamic programming methods using function approximators. The course starts with a concise introduction to Markov Decision Processes and optimal control problems, in order to build the foundation. We present an extensive review of state-ofthe- art approaches to dynamic programming and reinforcement learning with approximations. Theoretical guarantees are discussed on the solutions obtained, and numerical examples and applications are used to illustrate the properties of the individual methods.

NYU - Polytechnic School of Engineering

Spring 2015 - 24h (8 lectures)