CS 747: Foundations of Intelligent and Learning Agents
(Picture source: https://elephantcountry.org/sites/default/files/styles/main_image_800width/public/images/2016-07/elephant-funny.jpg)
Office: Room 220, New CSE Building
Lectures will be held during Slot 11: 3.30 p.m. – 4.55
p.m. Tuesdays and Fridays in LA 202. Office
hours will immediately follow class and be up to 5.30 p.m. on
Tuesdays and Fridays. Meetings can also be arranged by
Today's computing systems are becoming increasingly adaptive and
autonomous: they are akin to intelligent, decision-making
"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 multi-armed bandits; (3) Markov Decision
Problems and planning; (4) reinforcement learning; (5) multi-agent
systems and multi-agent learning; and (6) case studies.
The course will adopt a "hands-on" 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 "end-to-end" 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
The course is open to all Ph.D. students, all masters students, and
undergraduate/dual-degree students in their fourth (or higher) year of
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
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.
Grades will be based on four programming assignments, each worth 10
marks; a course project worth 20 marks; a mid-semester examination
worth 15 marks; and an end-semester examination worth 25 marks. The
course project will be undertaken in groups of size at most 4; all
other assessments will be based on individual work.
The programming assignments and project must be turned in through
Moodle. Late submissions will not be evaluated; they will receive no
Students auditing the course must score 50 or more marks in the
course to be awarded an "AU" grade.
- Marks for the assessments will be maintained on the class Moodle
page. Students who do not have an account on Moodle must send the
instructor a request by e-mail with their roll number/employee
- All programming assignments will be evaluated on the Software
Lab 2 machines. Students who do not have a CSE computing account
should request for access
Students are expected to adhere to the highest standards of
integrity and academic honesty. Academic violations, as detailed
below, will be dealt with strictly, in accordance with the
actions for academic malpractice.
Copying or consulting any external sources during the examinations will
be treated as cheating.
Students are expected to work alone on all the programming
assignments. They may not share code or consult with classmates (or
anybody other than the instructor and TAs) about their solutions. They
also may not look at solutions to the given assignment or related ones
on the Internet. Violations will be considered acts of dishonesty.
Students are allowed to use resources on the Internet for
programming (say to understand a particular command or a data
structure), and also to understand concepts (so a Wikipedia page or
someone's lecture notes or a textbook can certainly be consulted). It
is also okay to use libraries or code snippets for portions unrelated
to the core logic of the assignment—typically for operations
such as moving data, network communication, etc. However, students
must cite every resource consulted or used, whatever be the
reason, in a file named
references.txt, which must be
included in the submission. Failure to list any source will be
considered an academic violation.
Texts and References
Reinforcement Learning: An Introduction, Richard S. Sutton and
Andrew G. Barto, 2nd edition
Algorithms for Reinforcement Learning, Csaba Szepesvári,
Morgan & Claypool,
Selected research papers.
Stochastic Approximation Method
Herbert Robbins and Sutton Monro, 1951
Efficient Adaptive Allocation Rules
T. L. Lai and Herbert Robbins, 1985
Christopher J. C. H. Watkins and Peter Dayan, 1992
Reinforcement Learning with Replacing Eligibility Traces
Satinder P. Singh and Richard S. Sutton, 1996
Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results
Sridhar Mahadevan, 1996
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
Results for Single-Step On-Policy Reinforcement-Learning Algorithms
Satinder Singh, Tommi Jaakkola, Michael L. Littman, and Csaba Szepesvári, 2000
Analysis of the Multiarmed Bandit Problem
Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer, 2002
Bandit based Monte-Carlo Planning
Levente Kocsis and Csaba Szepesvári, 2006
Learning Tetris Using the Noisy Cross-Entropy Method
István Szita and András Lőrincz, 2006
Tree-based Batch Mode Reinforcement Learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel, 2005
Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning
Shivaram Kalyanakrishnan, Yaxin Liu, and Peter Stone, 2007
Batch Reinforcement Learning
in a Complex Domain
Shivaram Kalyanakrishnan and Peter Stone, 2007
An Empirical Evaluation of Thompson Sampling
Olivier Chapelle and Lihong Li, 2011
Learning Methods for Sequential Decision Making with Imperfect Representations
Shivaram Kalyanakrishnan, 2011
The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond
Aurélien Garivier and Olivier Cappé, 2011
Human-level 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
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, and Demis Hassabis, 2017.
Efficiency Fairness Tradeoff in Battery Sharing
Karan N. Chadha, Ankur A. Kulkarni, and Jayakrishnan Nair, 2019.
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 primarily be used for recording grades.
E-mail is the best means of communicating with the instructor;
students must send e-mail with "[CS747]" in the header, with a copy
marked to the TAs.
August 2: Welcome; Introduction to the course; Coin-tossing game.
August 6: Multi-armed Bandits.
Reading: Sections 2, 2.1, 2.2, 2.3, Sutton and Barto (2018).
Summary: Definition of stochastic multi-armed bandit; Definition of
algorithm, ε-first and ε-greedy algorithms; Graph of
E[rt] versus t.
August 9: Multi-armed Bandits.
Reading: Section 1, Figure 1, Theorem 1, Auer et al. (2002).
Summary: Definition of regret; Achieving sublinear regret with GLIE
(Greedy in the Limit with Infinite Exploration) sampling and in
particular, εt-greedy sampling; Lai and Robbins's
lower bound on regret; UCB algorithm.
Reference: Class Note 1; Theorem 1, Lai and Robbins (1985).
August 13: Multi-armed Bandits.
Reading: Wikipedia page
Inequality; Sections 1–3, Garivier and Cappé (2011);
Chapelle and Li (2011).
Summary: Hoeffding's Inequality; KL-UCB; Thompson Sampling.
August 16: Multi-armed Bandits.
Summary: Proof of upper bound on the regret of UCB.
August 20: Markov Decision Problems.
Reading: Chapter 3, Sutton and Barto (2018).
Summary: Definition of Markov Decision Problem, policy, and value
function; representing MDPs through state transition diagrams;
existence of optimal policy.
Reference: MDP example used in class.
August 23: Markov Decision Problems.
Summary: Reward models (infinite discounted, total, finite horizon,
average); Episodic and continuing tasks; Discounting; Existence of
optimal policy; Derivation of Bellman's Equations.
Reference: Section 2.2, Mahadevan (1996).
August 27: Markov Decision Problems.
Reading: Appendix A, Szepesvári (2009).
Summary: Bellman's Optimality Equations; Bellman Operator and
Bellman Optimality Operator; Banach's Fixed Point Theorem; Proof of
contraction under max norm.
August 30: Markov Decision Problems.
Summary: Value Iteration; Linear Programming for MDP Planning; Policy Improvement Theorem and Policy Iteration.
September 3: Reinforcement Learning.
Reading: Class Note 2.
Summary: The Reinforcement Learning problem; Assumption on reachability of states; Model-based learning algorithm for acting optimally in the limit.
September 13: Invited Talk, Ankur A. Kulkarni, Efficiency Fairness Tradeoff in Battery Sharing.
Reference: Chadha et al., 2019.
September 19: Mid-semester examination.
September 24: Reinforcement Learning.
Reading: Sections 5, 5.1, 5.2, 5.3, Sutton and Barto (2018); Class Note 3.
Summary: Monte Carlo prediction; First Visit and Every Visit Monte Carlo methods; Monte Carlo ES algorithm for control.
September 27: Invited talk by Harshad Khadilkar: RL
applications in the real world: Some use cases in industrial
October 1: Reinforcement learning.
Summary: Maximum likelihood estimates and least squares estimates;
on-line implementation of Monte Carlo policy evaluation;
bootstrapping; TD(0) algorithm; Convergence of batch TD(1) and batch TD(0) algorithms.
Reading: Sections 6, 6.1, 6.2, 6.3, Sutton and Barto (2018).
Reference: Robbins and Monro (1951).
October 4: Reinforcement learning.
Reading: Sections 7, 7.1, 7.2, 7.3, 12, 12.1, 12.2, 6.4, 6.5, 6.6,
Sutton and Barto (2018).
Summary: n-step returns; TD(λ) algorithm; Model-free control:
Sarsa, Q-learning, Expected Sarsa.
References: Watkins and Dayan (1992), Singh et al. (2000).
October 11: Reinforcement learning.
Reading: Sections 9, 9.1, 9.2, 9.3, 9.4, 11.4, Sutton and Barto
Summary: Need for generalisation in practice; soccer as illustrative
example; Linear function approximation and geometric view.
References: Section 2.1, Kalyanakrishnan et al. (2007).
October 15: Reinforcement learning.
Reading: Section 9.5, 12.2, 12.7, Sutton and Barto (2018).
Summary: Linear TD(λ); Tile coding.
October 18: Reinforcement learning.
Reading: Sections 10, 10.1, 10.2, 11, 11.1, 11.2, 11.3, Sutton and
Summary: Control with function approximation; Tsitsiklis and Van Roy's
counter-example: divergence of off-policy bootstrapping with linear
function approximation; Policy Search; Comparison of value
function-based methods and policy search methods in the game of
References: Chapter 4, Kalyanakrishnan (2011), Szita and Lőrincz (2006)
October 22: 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 25: Reinforcement learning.
Reading: Sections 13.4, 13.5, Sutton and Barto (2018), Kalyanakrishnan and Stone (2007).
Summary: REINFORCE with a baseline; Actor-critic methods; Batch RL; Experience replay; Fitted Q Iteration.
Reference: Ernst, Geurts, and Wehenkel (2005).
October 28: No class meeting.
Viewing: The Chip vs The Chess Master; AlphaGo.
November 1: Reinforcement learning.
Reading: Sections 8, 8.1, 8.2, 8.3, 8.4, Sutton and Barto (2018).
Summary: Model-based Reinforcement Learning; Dyna-Q; Tree search.
November 5: Reinforcement Learning.
Reading: Sections 8.10, 8.11, Sutton and Barto (2018); Randløv and Alstrøm (1998); Ng et al. (1999).
Summary: Roll-out Algorithms; Monte Carlo Tree Search (UCT); Reward shaping
Reference: Kocsis and Szepesvári, 2006
November 8: Reinforcement Learning.
Reading: Silver et al. (2016).
Summary: DQN algorithm on ATARI games; AlphaGo; AlphaGo Zero; AlphaZero.
Reference: Mnih et al. (2015); Silver et al. (2017); Wikipedia entry on AlphaZero.
November 13: End-semester Examination.
Programming Assignment 1 (10 marks).
Announced August 24, 2019; due 11.55 p.m., September 3, 2019.
Programming Assignment 2 (10 marks).
Announced September 5, 2019; due 11.55 p.m., September 15, 2019.
Project Proposal (5 marks).
Announced September 24, 2019; due 11.55 p.m., October 9, 2019.
Programming Assignment 3 (10 marks).
Announced October 18, 2019; due 11.55 p.m., October 28, 2019.
Project (15 marks).
Announced October 18, 2019; due 11.55 p.m., November 23, 2019.
Programming Assignment 4 (10 marks).
Announced October 29, 2019; due 11.55 p.m., November 8, 2019.