CS 747: Foundations of Intelligent and Learning Agents
(Autumn 2016)
(Picture source: http://www.nature.com/polopoly_fs/7.33483.1453824868!/image/WEB_Go1.jpg_gen/derivatives/landscape_630/WEB_Go1.jpg.)
Instructor
Shivaram Kalyanakrishnan
Office: Room 220, New CSE Building
Phone: 7704
Email: shivaram@cse.iitb.ac.in
Teaching Assistants
Samiran Roy
Office: 402, New CSE Building, Desk 39
Email: samiranroy@cse.iitb.ac.in
A. Siddharth
Office: 402, New CSE Building, Desk 40
Email: siddarth@cse.iitb.ac.in
Ashish Ramteke
Office: SynerG Lab, KReSIT Building, Desk B2
Email: ashishr@cse.iitb.ac.in
Class
Lectures will be held in F. C. Kohli Auditorium, KReSIT Building,
during Slot 6: 11.05 a.m. – 12.30 p.m. Wednesdays and Fridays.
Office hours will immediately follow class and be up to 1.15
p.m. on Wednesdays and Fridays. Meetings can also be arranged by
appointment.
Course Description
Today's computing systems are becoming increasingly adaptive and
autonomous: they are akin to intelligent, decisionmaking
``agents''. With its roots in artificial intelligence and machine
learning, this course covers the foundational principles of designing
such agents. Topics covered include: (1) agency, intelligence, and
learning; (2) exploration and multiarmed bandits; (3) Markov Decision
Problems and planning; (4) reinforcement learning; (5) search; (6)
multiagent systems and multiagent learning; and (7) case
studies.
The course will adopt a ``handson'' approach, with programming
assignments designed to highlight the relationship between theory and
practice. Case studies, as well as invited talks from experts, will
offer an ``endtoend'' view of deployed agents. It is hoped that
students can apply the learnings from this course to the benefit of
their respective pursuits in various areas of computer science and
related fields.
Prerequisites
The course is open to all Ph.D. students, all masters students, and
undergraduate/dualdegree students in their fourth (or higher) year of
study.
The course does not formally have other courses as
prerequisites. However, class lectures and assignments will assume
that the student is comfortable with probability and algorithms. The
course has an intensive programming component: based on ideas
discussed in class, the student must be able to independently design,
implement, and evaluate programs in a language of his/her choice. The
student must be prepared to spend a significant amount of time on the
programming component of the course.
Evaluation
Grades will be decided based on four programming assignments, each
worth 10 marks; a programming project worth 20 marks; a midsemester
examination worth 15 marks; and an endsemester examination worth 25
marks.
The programming assignments and project must be turned in through Moodle.
Students auditing the course must score 50 or more marks in the
course to be awarded an ``AU'' grade.
Academic Honesty
Students are expected to adhere to the highest standards of
integrity and academic honesty. Acts such as copying in the
examinations and sharing code for the programming assignments will be
dealt with strictly, in accordance with the
institute's procedures
and disciplinary
actions for academic malpractice.
Texts and References
Reinforcement Learning: An Introduction, Richard S. Sutton and
Andrew G. Barto, MIT Press,
1998. Online
version.
Artificial Intelligence: A Modern Approach, Stuart J. Russell and
Peter Norvig, 3rd edition, PrenticeHall, 2009.
Algorithms for Reinforcement Learning, Csaba Szepesvári,
Morgan & Claypool,
2009. Online
version.
Dynamic Programming and Optimal Control, Volume II, Dimitri
P. Bertsekas, 4th edition, Athena Scientific, 2012.
Regret Analysis of Stochastic and Nonstochastic Multiarmed Bandit
Problems, Sébastien Bubeck and Nicolò CesaBianchi, Foundations and
Trends in Machine Learning, Volume 5, Number 1,
2012. Online
version.
Selected research papers.

A
Stochastic Approximation Method
Herbert Robbins and Sutton Monro, 1951

Probability
inequalities for sums of bounded random variables
Wassily Hoeffding, 1963

Asymptotically
Efficient Adaptive Allocation Rules
T. L. Lai and Herbert Robbins, 1985

Markov games as a framework for multiagent reinforcment learning
Michael L. Littman, 1994

Foraging in an Uncertain Environment Using Predictive Hebbian Learning
P. Read Montague, Peter Dayan, and Terrence J. Sejnowski, 1994

On the Complexity of Solving Markov Decision Problems
Michael L. Littman, Thomas L. Dean, and Leslie Pack Kaelbling, 1995

Reinforcement Learning with Replacing Eligibility Traces
Satinder P. Singh and Richard S. Sutton, 1996.

Dopamine neurons report and error in the temporal prediction of reward during learning
Jeffrey R. Hollerman and Wolfram Schultz, 1998

Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra, 1998

Learning to Drive a Bicycle using Reinforcement Learning and Shaping
Jette Randløv and Preben Alstrøm, 1998

Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y. Ng, Daishi Harada, and Stuart Russell, 1999

Convergence
Results for SingleStep OnPolicy ReinforcementLearning Algorithms
Satinder Singh, Tommi Jaakkola, Michael L. Littman, and Csaba Szepesvári, 2000

Policy
Gradient Methods for Reinforcment Learning with Function
Approximation
Richard S. Sutton, David McAllester, Satinder Singh, and Yishay
Mansour, 2000.

Rational and Convergent Learning in Stochastic Games
Michael Bowling and Manuela Veloso, 2001

Finitetime
Analysis of the Multiarmed Bandit Problem
Peter Auer, Nicolò CesaBianchi, and Paul Fischer, 2002

The RandomFacet Simplex Algorithm on Combinatorial Cubes
Bernd Gärtner, 2002

Nash QLearning For GeneralSum Stochastic Games
Junling Hu and Michael P. Wellman, 2003.

Evolving Neural Networks through
Augmenting Topologies
Kenneth O. Stanley and Risto Miikkulainen, 2002

Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence
Pamela McCorduck, 2004

Action
Elimination and Stopping Conditions for the MultiArmed Bandit and
Reinforcement Learning Problems
Eyal EvenDar, Shie Mannor,
and Yishay Mansour, 2006

Bandit based MonteCarlo Planning
Levente Kocsis and Csaba Szepesvári, 2006

If multiagent learning is the answer, what is the question?
Yoav Shoham, Rob Powers, and Trond Grenager, 2006

Batch Reinforcement Learning in a Complex Domain
Shivaram Kalyanakrishnan and Peter Stone, 2007

Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study
Shivaram Kalyanakrishnan, Yaxin Liu, and Peter Stone, 2007

REGAL: A Regularization based Algorithm for Reinforcement Learning in
Weakly Communicating MDPs
Peter L. Bartlett and Ambuj Tewari, 2009.

Is it
Enough to Get the Behaviour Right?
Hector J. Levesque,
2009

Artificial Intelligence: A Modern Approach
Stuart Russell and Peter Norvig, 2009

UT Austin Villa 2011: A
Champion Agent in the RoboCup 3D Soccer Simulation Competition
Patrick MacAlpine, Daniel Urieli, Samuel Barrett, Shivaram
Kalyanakrishnan, Francisco Barrera, Adrian LopezMobilia, Nicolae Ştiurcă, Victor Vu, and Peter Stone, 2012

The Quest for Artificial Intelligence: A History of Ideas and Achievements
Nils J. Nilsson, 2010

An Empirical Evaluation of Thompson Sampling
Olivier Chapelle and Lihong Li, 2011

The KLUCB Algorithm for Bounded Stochastic Bandits and Beyond
Aurélien Garivier and Olivier Cappé, 2011

Learning Methods for Sequential Decision Making with Imperfect Representations
Shivaram Kalyanakrishnan, 2011

Thompson Sampling: An Asymptotically Optimal FiniteTime Analysis
Emilie Kaufmann, Nathaniel Korda, and Rémi Munos, 2012

BatchSwitching
Policy Iteration
Shivaram Kalyanakrishnan, Utkarsh Mall, and Ritish Goyal, 2016a

Randomised Procedures for Initialising and Switching Actions in Policy Iteration
Shivaram Kalyanakrishnan, Neeldhara Misra, and Aditya Gopalan, 2016b

Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis, 2016
Communication
This page will serve as the primary source of information regarding
the course, the schedule, and related announcements. The Moodle page
for the course will be used for sharing resources for the lectures and
assignments, and also for recording grades.
Email is the best means of communicating with the instructor;
students must send email with ``[CS747]'' in the header, with a copy
marked to the TAs.
Schedule

July 20: Welcome, Introduction to the course.

July 22: Multiarmed Bandits.
Reading: Sections 2–2.2, Sutton and Barto (1998); Chapter 1, Bubeck and CesaBianchi (2012).
Summary: Cointossing game, definition of stochastic multiarmed bandit, definition of
algorithm, εgreedy and εfirst algorithms, definition of regret.

July 27: Multiarmed Bandits.
Reading: Auer et al. (2002).
Summary: Graph of E[r^{t}] versus t, GLIE (Greedy in the Limit with
Infinite Exploration) property, Lai and Robbins's lower bound on
regret, UCB algorithm.
References: Section 2.2, Singh et al. (2000); Lai and Robbins (1985).

July 29: Multiarmed Bandits.
Reading: Wikipedia page
on Hoeffding's
Inequality.
Summary: Hoeffding's Inequality, first part of proof of regret bound for UCB (bounding the
probability of pulling a suboptimal arm beyond a threshold number of pulls).
References: Theorem 2, Hoeffding (1963), Auer et al. (2002).

August 3: Multiarmed Bandits.
Reading: Sections 1–3, Garivier and Cappé (2011).
Summary: Second part of proof of regret bound for UCB (bounding the
expectation of the number of pulls of a suboptimal arm), history of
theoretical results on stochastic bandits.
Reference: Auer et al. (2002).

August 5: Multiarmed Bandits.
Reading: Chapelle and Li (2011).
Summary: Revision of portion of UCB proof, Thompson Sampling,
discussion of bandit variations: adversarial setting, pure
exploration, modeling dependence between arms.
References: Kaufmann et al. (2012); Chapters 3–5, Bubeck and
CesaBianchi (2012), EvenDar et al. (2006).

August 10: MDP Planning.
Reading: Section 1, Kalyanakrishnan et al. (2016b).
Summary: Definition of Markov Decision Problem, policy, and value
function; representing MDPs through state transition diagrams;
existence of an optimal policy.

August 12: MDP Planning.
Reading: Chapter 3, Sutton and Barto (1998).
Summary: Unifying episodic and continuous tasks; ``design'' of states,
actions, and rewards; illustrative examples (chess, soccer, and
helicopter hovering); introduction to Bellman's Equations.

August 17: MDP Planning.
Reading: Chapter 4, Sutton and Barto (1998).
Summary: Solution of Bellman's Equations by variable elimination and
by iteration; Bellman operator and its fixed point; Bellman's
Optimality Equations; Bellman Optimality operator and its fixed
point.

August 19: MDP Planning.
Reading: Littman et al. (1995).
Summary: Value Iteration, Linear Programming formulation for MDP
planning, action value functions.
Reference: Gärtner (2002).

August 24: MDP Planning.
Reading: Section 2, Kalyanakrishnan et al. (2016b).
Summary: Policy Iteration, first part of proof of convergence and
correctness.

August 26: MDP Planning.
Reading: Chapter 4, Sutton and Barto (1998).
Summary: Second part of proof of convergence and correctness, upper
and lower bounds for Policy Iteration.
Reference: Section 2, Kalyanakrishnan et al. (2016a); Section 2,
Kalyanakrishnan et al. (2016b).

August 31: Agency, Intelligence, and Learning.
Summary: Notions of intelligence and artificial intelligence, summary
of developments up to 1956.
References: McCorduck (2004), Nilsson (2010).

September 2: Agency, Intelligence, and Learning.
Summary: Summary of developments in AI starting 1956.
References: Chapter 1, Russell and Norvig (2009); Levesque (2009).

September 10: Midsemester examination. 8.30 a.m. – 10.30 a.m., 101/103/105 New CSE Building.

September 14: Reinforcement learning.
Reading: Introduction to Chapter 5, Sutton and Barto (1998).
Summary: The Reinforcement Learning Problem; Ergodic, Unichain, and Communicating MDPs; modelbased learning algorithm for acting optimally in the limit.
Reference: Section 1, Bartlett and Tewari (2009).

September 16: Reinforcement learning.
Reading: Chapter 5, Sutton and Barto (1998).
Summary: Monte Carlo policy evaluation, firstvisit and everyvisit
Monte Carlo methods, PAC policy evaluation.
Reference: Sutton and Singh (1996), Class Note 1 (see Moodle).

September 21: Reinforcement learning.
Summary: Online implementation of Monte Carlo policy evaluation,
bootstrapping, TD(0) algorithm.
Reference: Robbins and Monro (1951).

September 23: Reinforcement learning.
Reading: Sections 6.1–6.5, Sutton and Barto (1998).
Summary: Maximum likelihood estimates and least squares estimates,
convergence of batch TD(0) and batch TD(1), temporal difference
learning for control.

September 28: Reinforcement learning.
Reading: Chapter 7 (except Section 7.7), Sutton and Barto (1998).
Summary: TD learning in animals, TD(lambda) algorithm.
References: Wikipedia
page on Classical Conditioning; Montague, Dayan, and Sejnowski
(1994); Hollerman and Schultz (1998).

September 30: Reinforcement learning, Case Study.
Summary: Sarsa(0) and Q(0), RoboCup: A Grand Challenge for AI.
References: Kalyanakrishnan et al. (2007), MacAlpine et al. (2012).

October 5: Reinforcement learning.
Reading: Sections 8.1–8.3, Sutton and Barto (1998).
Summary: Need for generalisation and function approximation, Linear TD(lambda).

October 7: Reinforcement learning.
Reading: Sections 8.4–8.7, Sutton and Barto (1998).
Summary: Tsitsiklis and Van Roy's counterexample, divergence of
offpolicy bootstrapping with linear function approximation.

October 14: Reinforcement learning.
Reading: Section 8.3.2, Sutton and Barto (1998).
Summary: Geometric view of linear function approximation, tile
coding.

October 19: Reinforcement learning.
Reading: Chapter 4, Kalyanakrishnan (2011).
Summary: Policy search, comparison with value functionbased methods,
Tetris as an illustrative example.

October 21: Reinforcement learning.
Reading: Introduction and Section 1, Sutton et al. (2000); Chapter 9,
Sutton and Barto (1998); Kalyanakrishnan and Stone (2007).
Summary: Policy gradient methods, modelbased reinforcement learning,
batch reinforcement learning.

October 26: Reinforcement learning.
Reading: Silver et al. (2016).
Summary: AlphaGo, Monte Carlo Tree Search.
Reference: Kocsis and Szepesvári (2006).

October 28: Multiagent learning.
Reading: Littman (1994).
Summary: Matrix games, Minimaxoptimality, Markov games, MinimaxQ algorithm.
References: Bowling and Veloso (2001), Hu and Wellman (2003), Shoham et al. (2006).

November 2: Miscellaneous topics.
Summary: POMDPs, neuroevolution, reward shaping.
References: Kaelbling et al. (1998), Randløv and Alstrøm (1998), Stanley and Miikkulainen (2002), Ng et al. (1999).
Assignments

August 16: Programming assignment 1 announced on
Moodle. Submission deadline: 11.55 p.m., September 1, 2016.

September 16: Programming assignment 2 announced on
Moodle. Submission deadline: 11.55 p.m., September 25, 2016.

September 30: Programming assignment 3 announced on
Moodle. Submission deadline:
11.55 p.m., October 10, 2016 11.55 p.m., October 12, 2016.

October 15: Programming assignment 4 announced on
Moodle. Submission deadline: 11.55 p.m., October 25, 2016.

October 16: Final project announced on Moodle. Submission
deadline: 11.55 p.m., November 15, 2016.