Stat234:
Sequential Decision Making
1.
Course Description
This graduate course will
focus on reinforcement learning algorithms and sequential decision making
methods with special attention to how these methods can be used in mobile
health. Reinforcement learning is the
area of machine learning which is concerned with sequential decision
making. We will focus on the areas of
sequential decision making that concern both how to select optimal actions as well
as how to evaluate the impact of these actions.
The choice of action is operationalized via a policy. A policy is a (stochastic) deterministic
mapping from the available data at each time t into (a probability space over)
the set of actions. We will consider both off-line and on-line methods for
learning good policies.
Mobile health is an area
that lies within multiple scientific disciplines including: statistical
science, computer science, behavioral science and cognitive neuroscience. This
makes for very exciting interdisciplinary science! Smartphones and wearable
devices have remarkable sensing capabilities allowing us to understand the
context in which a person is at a given moment. These devices also have the
ability to deliver treatment actions tailored to the specific needs of users in
a given location at a given time. Figuring out when and in which context, which
treatment actions to deliver can assist people in achieving their longer term
health goals. In the last 15-20 minutes
of each class we will brainstorm about how the methods we discussed during that
class might be useful in mobile health.
This course will cover the
following topics: Markov Decision
Processes, on-policy and off-policy RL and topics in RL that currently appear
most useful if one is interested in mobile health (Experience Replay,
Hierarchical RL). Most of these topics
have to do with speeding up the rate at which we can learn a good policy. Other topics from RL that are important in
mobile health but that we won’t cover are (you could consider in your class
project): 1) transfer learning (using data on other similar users to enable
faster learning); 2) non-stationarity (dealing with slowly changing or abrupt
changes in user behavior); 3) interpretability of policies (enabling
communication with behavioral scientists by making connections to behavioral
theories); 4) using approximate system dynamic models to speed up learning and
5) Bayesian RL.
Our course website is http://people.seas.harvard.edu/~samurphy/teaching/stat234spring2018/index.htm
See Canvas for scribing templates
See our Github repository for code https://github.com/samurphy11/Stat234
3.
Grading
4.
Teaching
Susan Murphy is Professor of
Statistics and Computer Science & Radcliffe Alumnae Professor at the
Radcliffe Institute, Harvard University.
She earned her Ph.D. at the University of North Carolina, Chapel
Hill. She is a MacArthur Fellow, a
Fellow of National Academy of Sciences & a Fellow of National Academy of
Medicine. Her work is focused on
sequential decision making, causal inference and new ways for carrying out
sequential experimentation particular focus on mobile health. Her email address is samurphy@fas.harvard.edu. Susan Murphy's office hours are by
appointment during 7-8pm Wednesdays and 5-5:30pm on Fridays in Science Center
703.
Our head teaching fellow is Ryan Lee. Ryan is a G2
student in the Department of Statistics, Harvard. His email address is wil896@g.harvard.edu. Ryan’s
office hours are TBA.
5.
Postdoctoral Fellows and Graduate Students
Members
of the Statistical
Reinforcement Learning Lab will be giving some of the lectures. They include:
Walter Dempsey.
Walter received his Ph.D. in Statistics at University of Chicago. His email address is wdempsey@fas.harvard.edu.
Peng Liao.
Peng is a 4th Ph.D. graduate student at University of
Michigan and an Associate in the Department of Statistics at Harvard
University. His email address is pengliao@g.harvard.edu.
Tianchen Qian. Tianchen received
his Ph.D. in Biostatistics from Johns Hopkins University. His email address is qiantianchen.thu@gmail.com.
Mashfiqui (Mash) Rabbi. Mash received his Ph.D. in Information
Science at Cornell University. His email
address is mrabbi@fas.harvard.edu.
Ashley Walton.
Mash received her Ph.D. in Experimental Psychology at the University of
Cincinnati. Her email address is ashley_walton@fas.harvard.edu.