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ECE-517: Reinforcement Learning in Artificial Intelligence
- Course Proposal Course Description
Principles and methods for reinforcement learning and sequential stochastic
control; Markov decision problems; dynamic programming; temporal
difference learning; design considerations for hardware and software.
Prereq: Consent of instructor. Credit: 3.
Effective: Fall 2007
Textbook
R. Sutton and A. Barto, “Reinforcement Learning: An Introduction,” MIT Press, 1998.
Course Outline
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Foundations of machine learning & autonomous cognitive systems
Markov decision processes (MDP)
Artificial neural networks
Dynamic programming
Temporal difference (TD) learning
Monte Carlo reinforcement learning methods
Eligibility traces
Hardware & software implementation considerations
Supporting Information
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Rationale: This new graduate course will provide students with indepth knowledge and understanding of diverse theoretical and
practical aspects pertaining to stochastic control and applied machine
learning.
Course has been offered twice (Fall 2005, Fall 2006), with total
enrollment of 32 students
Course format and location: Standard format, on-campus.
Impact on other academic units: None
Financial impact: None.