University of Delaware 3 credits ELEG
Modeling and controlling real-world dynamical systems requires understanding the underlying stochastic nature of observations and their operations. The underlying mechanics may be completely unknown or partially observable, and one has to learn how to act or control from incomplete information. This can involve model-based and model-free approaches. This course will cover the fundamentals of dynamical modeling and reinforcement learning. Key concepts and approaches covered include dynamic programming, stochastic function approximation, the exploration-exploitation trade-off, temporal difference learning, Q-learning, and actor-critic. Applications in contexts involving multiple agents, human-in the-loop, and human feedback will be explored along with issues of safety and alignment. Prerequisite: ELEG 405 or ELEG 815 or CISC 484 or MEEG 311.
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