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Course Detail

Course Name Reinforcement Learning
Course Code 23AID312
Program B.Tech in Artificial Intelligence and Data Science
Semester 6
Credits 3
Campus Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati

Syllabus

Unit 1

Introduction to Reinforcement Learning – History of Reinforcement Learning – Elements of Reinforcement Learning – Limitations and scope

Unit 2

Multi-armed Bandits – Finite Markov Decision Processes – Dynamic Programming – Policy evaluation – Policy improvement – Policy Iteration – Value Iteration

Unit 3

Monte Carlo Methods – Monte Carlo prediction – Monte Carlo control – Incremental Implementation – Temporal-Difference Learning – TD prediction – Q-Learning – n-step Bootstrapping

Unit 4

Planning and Learning with Tabular Methods – Models and planning – Prioritized sweeping – Trajectory sampling – Heuristic search – Rollout algorithms

Objectives and Outcomes

Course Objectives

  • This course will provide a solid introduction to the field of reinforcement learning.
  • It will also make the students learn about the core challenges and approaches, including exploration and exploitation.
  • The course will make the students well versed in the key ideas and techniques for reinforcement learning.

Course Outcomes

After completing this course, students will be able to

CO1

Formulate an application problem as a reinforcement learning problem

CO2

Implement common reinforcement learning algorithms using Python/Matlab

CO3

Evaluate reinforcement learning algorithms on the metrics such as regret, sample complexity, computational complexity, empirical performance, and convergence

CO4

Evaluate different approaches for addressing exploration vs exploitation challenge in terms of performance, scalability, complexity of implementation, and theoretical guarantees

CO-PO Mapping

PO/PSO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

PSO1

PSO2

PSO3

CO

CO1

2

2

2

2

2

–

–

1

–

–

–

2

3

–

1

CO2

2

2

2

2

2

–

–

1

–

–

–

2

3

–

1

CO3

1

1

1

1

1

–

–

1

–

–

–

1

–

3

1

CO4

1

1

1

1

1

–

–

1

–

–

–

1

–

3

1

Evaluation Pattern

Evaluation Pattern

Assessment

Internal/External

Weightage (%)

Assignments (minimum 2)

Internal

30

Quizzes (minimum 2)

Internal

20

Mid-Term Examination

Internal

20

Term Project/ End Semester Examination

External

30

Text Books / References

Text Books / References

Richard.S.Sutton and Andrew G.Barto, Reinforcement Learning, MIT Press, Second Edition, 2018

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