Adaptive Control and Reinforcement Learning, S11
SyllabusAdaptiveControlAndReinforcement
16-899C ACRL:
Adaptive Control and Reinforcement Learning
Decision Making, Planning and Control
Time and Day: Spring, 2011, 4:30-5:50, Tuesday and Thursday, NSH 3002
Instructors: Drew Bagnell (dbagnell+16899@ri.cmu.edu)
TA: Stephane Ross
Why?
Machine learning has escaped from the cage of perception. A growing number of state-of-the-art systems from field robotics, acrobatic autonomous helicopters, to the leading computer Go player and walking robots rely upon learning techniques to make decisions. This change represents a truly fundamental departure from traditional classification and regression methods as such learning systems must cope with
a) their own effects on the world,
b) sequential decision making and long control horizons, and
c) the exploration and exploitation trade-off. In the last 5 years, techniques and understanding of these have developed dramatically. One key to the advance of learning methods has been a tight integration with
optimization techniques, and as such our case studies will focus on this.
What? (Things we will cover)
Planning and Optimal Control Techniques
Reinforcement Learning and Adaptive Control
Imitation Learning and Inverse Optimal Control
Motion Planning, Trajectory Optimization, Operation Space Control
Exploration and Experiment Design
Policy Search Methods
Design for Learnability
Planning/Decision making under Uncertainty