Time and Place:
Thursdays 14:30-17:20 in RTH 422
- Introduction to reinforcement learning with view towards solving real-world problems
- State-of-the-art of learning control with reinforcement learning
- Projects possible with simulated or actual robots
This course will introduce and discuss machine learning methods for learning control, particularly with a focus on robotics, but also applicable to models of learning in biology and any other control process. The course will cover the basics of reinforcement learning with value functions (dynamic programming, temporal difference learning, Q-learning). The emphasis, however, will be on learning methods that scale to complex high dimensional control problems. Thus, we will cover function ap-proximation methods for reinforcement learning, policy gradients, probabilistic reinforcement learning, learning from trajectory trials, optimal control methods, stochastic optimal control methods, dynamic Bayesian networks for learning control, Gaussian processes for reinforcement learning, etc.
This class has a mixture of tutorial and seminar style, with reading assignment, paper presentations, and projects.
- 2 Paper Presentations per student (20% per presentation)
- Final Project (40%)
- Participation in Class (20%).
CS545, CS542 or CS567, or any other graduate level classes that provided the foundation of machine learning and robotics, or permission by instructor.
Dr. Stefan Schaal
University of Southern California
Ronald Tutor Hall RTH-401
Los Angeles, CA 90089-2520
phone: (213) 740 9418
email: (:email email@example.com :)
According to email arrangement with instructor (:email firstname.lastname@example.org :).
All students are required to abide by the USC code of Academic Integrity. Violation of that Code will be dealt with as described in SCAMPUS. If you have any questions about the responsibilities of either students, faculty, or graders under this policy, contact the instructor or the Office of Student Conduct.
Disabilities and Academic Accommodations:
Students requesting academic accommodations based on a disability are required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP when adequate documentation is filed. Please be sure the letter is delivered to the instructor (or TA) as early in the semester as possible. DSP is open Monday-Friday, 8:30-5:00. The office is in Student Union 301 and their phone number is (213) 740-0776.