CS 747: Foundations of Intelligent and Learning Agents
(Autumn 2017)
(Picture source: Learning to Drive a Bicycle using Reinforcement Learning and Shaping, Randløv and Alstrøm, 1998.)
Instructor
Shivaram Kalyanakrishnan
Office: Room 220, New CSE Building
Phone: 7704
Email: shivaram@cse.iitb.ac.in
Teaching Assistants
Abhilash Panicker
Email: abhilashp@cse.iitb.ac.in
Pragy Agarwal
Email: agar.pragy@gmail.com
Prashanth M
Email: 163050043@iitb.ac.in
Class
Lectures will be held in Room 103, New CSE 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) multiagent
systems and multiagent learning; and (6) 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. Introduction
to Probability by Grinstead and Snell is an excellent resource on
basic probability. Any student who is not comfortable with the
contents of chapters 1 through 7 (and is unable to solve the
exercises) is advised against taking CS 747.
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 based on four programming assignments, each
worth 10 marks; a course 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. Late submissions will not be evaluated; they will receive no
marks.
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, 2^{nd} edition (in progress)
2017. Online
version.
Reinforcement Learning: An Introduction, Richard S. Sutton and
Andrew G. Barto, MIT Press,
1998. Online
version.
Algorithms for Reinforcement Learning, Csaba Szepesvári,
Morgan & Claypool,
2009. Online
version.
Selected research papers.

A
Stochastic Approximation Method
Herbert Robbins and Sutton Monro, 1951

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

Qlearning
Christopher J. C. H. Watkins and Peter Dayan, 1992

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

Improving Elevator Performance Using Reinforcement Learning
Robert H. Crites and Andrew G. Barto, 1996

Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results
Sridhar Mahadevan, 1996

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

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

On the Complexity of Policy Iteration
Yishay Mansour and Satinder Singh, 1999

Evolutionary Algorithms for Reinforcement Learning
David E. Moriarty, Alan C. Schultz, and John J. Greffenstette, 1999

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

Algorithms for Inverse Reinforcement Learning
Andrew Y. Ng and Stuart Russell, 2000

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

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

Apprenticeship Learning via Inverse Reinforcement Learning
Pieter Abbeel and Andrew Y. Ng, 2004

Treebased Batch Mode Reinforcement Learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel, 2005

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

Learning Tetris Using the Noisy CrossEntropy Method
István Szita and András Lőrincz, 2006

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

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

Efficient Selection of Multiple Bandit Arms: Theory and Practice
Shivaram Kalyanakrishnan and Peter Stone, 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

Characterizing Reinforcement Learning Methods through Parameterized Learning Problems
Shivaram Kalyanakrishnan and Peter Stone, 2011

PAC Subset Selection in Stochastic Multiarmed Bandits
Shivaram Kalyankrishnan, Ambuj Tewari, Peter Auer, and Peter Stone, 2012

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

Understanding Machine Learning: From Theory to Algorithms
Shai ShalevSchwartz and Shai BenDavid, 2014

Humanlevel control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel
Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas
K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir
Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan
Wierstra, Shane Legg, and Demis Hassabis, 2015

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

Mastering the game of Go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis, 2017
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 19: Welcome, Introduction to the course.

July 21: Multiarmed Bandits.
Reading: Sections 2, 2.1, 2.2, 2.3, Sutton and Barto (2017).
Summary: Cointossing game, definition of stochastic multiarmed
bandit, definition of algorithm, εgreedy algorithm and
variants.

July 26: Multiarmed Bandits.
Reading: Sections 1, 2, Auer et al. (2002).
Summary: Graph of E[r^{t}] versus t, definition of regret,
achieving sublinear regret with epsilon_{t}greedy sampling, Lai and
Robbins's lower bound on regret, UCB algorithm.
Reference: Theorem 1, Lai and Robbins (1985).

July 28: Multiarmed Bandits.
Reading: Wikipedia page
on Hoeffding's
Inequality, Proof of Theorem 1, Auer et al. (2002).
Summary: Hoeffding's Inequality, crux of proof of regret bound for UCB (bounding the
probability of pulling a suboptimal arm beyond a threshold number of pulls).

August 2: Multiarmed Bandits.
Reading: Sections 1–3, Garivier and Cappé (2011),
Chapelle and Li (2011).
Summary: Second part of proof of regret bound for UCB (bounding the
expectation of the number of pulls of a suboptimal arm), KLUCB,
Thompson Sampling, discussion of bandit variations (adversarial setting, pure
exploration, modeling dependence between arms, nonstationarity).

August 4: 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, reward
models (infinite discounted, total, average, finite horizon),
existence of optimal policy.
Reference: Section 2.2, Mahadevan (1996).

August 9: MDP Planning.
Reading: Chapter 3, Sutton and Barto (2017).
Summary: Design of states, actions, and rewards; illustrative examples (soccer,
elevator control), derivation of Bellman's Equations, solution of
Bellman's Equations by variable elimination and by iteration,
Bellman's Optimality Equations.
Reference: Sections 1 and 2, Crites and Barto (1996).

August 11: MDP Planning.
Reading: Section 4.1, Littman, Dean, and Kaelbling (1995), Section
2.3, Szepesvári (2009).
Summary: Action value function, Value Iteration, Linear Programming
formulation for MDP planning, Bellman operator and its fixed point,
Bellman Optimality operator and its fixed point.
Reference: Wikipedia page
on Linear
Programming.

August 16: MDP Planning.
Reading: Section 2, Kalyanakrishnan et al. (2016b), Sections 1 and 2 of slides.
Summary: Strong bounds, Policy Iteration, proof of correctness.

August 18: MDP Planning.
Reading: Sections 3 and 4 of slides.
Summary: Complexity analysis of Howard's Policy Iteration, Mansour and
Singh's Randomised Policy Iteration, and Batchswitching Policy
Iteration on 2action MDPs.
References: Mansour and Singh (1999), Kalyanakrishnan et al. (2016a).

August 23: Invited talk by Manjesh Kumar Hanawal on adversarial bandits.
Reading: Lecture notes.
Reference: Chapter 21, ShalevSchwartz and BenDavid (2014).

August 30: Class canceled.

September 1: Reinforcement learning.
Reading: Class Note 1.
Summary: The Reinforcement Learning problem, Ergodic MDPs, modelbased
learning algorithm for acting optimally in the limit.
Reference: Section 10.3, Sutton and Barto (2017); Section 2.2, Singh et al. (2000).

September 6: Reinforcement learning.
Reading: Sections 5, 5.1, 5.2, 5.3, 5.4, Sutton and Barto (2017), Class Note 2.
Summary: Monte Carlo policy evaluation, firstvisit and everyvisit Monte Carlo
methods, PAC policy evaluation.

September 8: Reinforcement learning.
Summary: Maximum likelihood estimates and least squares estimates,
online implementation of Monte Carlo policy evaluation,
bootstrapping, TD(0) algorithm.
Reading: Sections 6, 6.1, 6.2, 6.3, Sutton and Barto (2017).
Reference: Robbins and Monro (1951).

September 8: Invited talk by Vijay Nadkarni (Visteon
Corporation): The Artificial Intelligence Revolution in Autonomous
Driving and the Reinvention of the Automotive Industry. 5.30
p.m. – 6.30 p.m., F. C. Kohli Auditorium, KReSIT Building.

September 12: Midsemester examination.

September 20: Reinforcement learning.
Reading: Sections 7, 7.1, 7.2, 7.3, 7.4, Sutton and Barto (1998).
Summary: TD learning in animals, TD(λ) algorithm.
References: Wikipedia
page on Classical Conditioning; Montague, Dayan, and Sejnowski
(1994); Hollerman and Schultz (1998).

September 22: Reinforcement learning.
Reading: Sections 6.4, 6.5, 6.6, Sutton and Barto (2017).
Summary: Modelfree control: Sarsa, Qlearning, Expected Sarsa; Need
for generalisation in practice; soccer as illustrative example.
References: Watkins and Dayan (1992), Singh et al. (2000), Section 2.1, Kalyanakrishnan et al. (2007).

September 27: Reinforcement learning, Invited talk.
Reading: Sections 9, 9.1, 9.2, 9.3, 9.4, 11.4, Sutton and Barto
(2017).
Summary: Linear TD(λ), Geometric view of linear function
approximation, Invited talk by Harshad Khadilkar: Current
industrial applications of reinforcement learning.
Reference: Slides.

September 29: Reinforcement learning.
Reading: Sections 9.5, 10, 10.1, 10.2, 11, 11.1, 11.2, 11.3, Sutton and
Barto (2017).
Summary: Control with function approximation, Tsitsiklis and Van Roy's
counterexample, divergence of offpolicy bootstrapping with linear
function approximation, Tile coding.

October 4: Reinforcement learning.
Reading: Sections 1, 2, 3, 4, 4.1, 4.2, 4.3, 4.4, 4.5, Kalyanakrishnan
and Stone (2011).
Summary: Tile coding (continued), Effect of representation on learning
methods, Comparison of Value Functionbased methods and Policy Search
methods on Parameterised Learning Problems.

October 6: Reinforcement learning.
Reading: Chapter 4, Kalyanakrishnan (2011).
Summary: Illustration of the effect of representation on Value
Functionbased methods and Policy Search methods in the game of
Tetris; Evolutionary algorithms.
References: Moriarty, Schultz, and Greffenstette (1999), Szita and Lőrincz (2006).

October 6: Reinforcement learning.
Reading: Sections 13, 13.1, 13.2, 13.3, Sutton and Barto (2017).
Summary: Brief review of tile coding, Policy gradient methods, Policy gradient theorem, REINFORCE.

October 13: Reinforcement learning.
Reading: Sections 13.4, 13.5, Sutton and Barto (2017), Kalyanakrishnan and Stone (2007).
Summary: REINFORCE with a baseline, Actorcritic methods, Spectrum of RL methods, Batch RL, Experience replay.
Reference: Appendices C, D, Kalyanakrishnan (2011).

October 18: Reinforcement learning.
Reading: Chapter 8, Sutton and Barto (2017).
Summary: Fitted Q Iteration, Modelbased RL, DynaQ, Trajectory sampling, Prioritised sweeping.
Reference: Ernst, Geurts, and Wehenkel (2005).

October 20: Reinforcement learning.
Reading: Kocsis and Szepesvári (2006).
Summary: Full and sample backups, Realtime Dynamic Programming, Monte Carlo Tree Search, UCT.

October 25: Reinforcement learning.
Reading: Mnih et al. (2015), Silver et al. (2016).
Summary: DQN on ATARI video games, AlphaGo.
Reference: Silver et al. (2017).

October 27: 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 1: Inverse reinforcement learning.
Reading: Ng and Russell (2000).
Summary: Inverse reinforcement learning problem, nonuniqueness of reward function, LP formulation.
Reference: Abbeel and Ng (2004).

November 3: Reward shaping.
Reading: Randløv and Alstrøm (1998), Ng et al. (1999).
Summary: Purpose of shaping rewards, bicycle control as example,
necessary and sufficient conditions for shaping rewards.

November 8: Pure exploration in multiarmed bandits.
Reading: Slides.
Summary: Pure exploration, PAC subset selection, LUCB algorithm.
References: Sections 1, 2, 3, Kalyanakrishnan and Stone (2010), Kalyanakrishnan et al. (2012).

November 22: Endsemester examination.
Assignments

Programming Assignment 1 (10 marks).
Announced August 4, 2017; due 11.55 p.m., August 14, 2017.

Programming Assignment 2 (10 marks).
Announced August 23, 2017; due 11.55 p.m., September 5, 2017.

Programming Assignment 3 (10 marks).
Announced September 29, 2017; due 11.55 p.m., October 10, 2017.

Project Proposal (5 marks).
Announced October 5, 2017; due 11.55 p.m., October 17, 2017.

Project (15 marks).
Announced October 23, 2017; due 11.55 p.m., November 23, 2017.

Programming Assignment 4 (10 marks).
Announced November 1, 2017; due 11.55 p.m., November 10, 2017.
Examination Papers from Previous Offerings