Courses Detail Information

ECE7606J – Stochastic Control and Reinforcement Learning

Instructor: Li Jin

Instructors (Faculty):

Credits: 3 credits



Control and optimization of discrete-time and continuous-time Markov processes. Probability model, convergence of random variables. Countable-state Markov chains, continuous-state Markov chains, Foster-Lyapunov stability theory, Markov decision processes, dynamic programming, Monte-Carlo method, temporal-difference method, approximate dynamic programming. Continuous-time Markov processes, Poisson processes, queuing theory, infinitesimal generator, piecewise-deterministic Markov processes. Applications include connected and autonomous vehicles, intelligent transportation systems, computer and communication systems, social networks, epidemics, and finance.

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Sample Syllabus