An Introduction to Reinforcement Learning

This thesis presents a new course textbook on reinforcement learning (RL) with a focus on algorithms and their properties. The textbook is suitable for a one-semester introductory undergraduate course on RL for students with prior experience in basic probability, linear algebra, and multivariable calculus. We systematically cover the fundamentals of Markov decision processes, optimal control, multi-armed bandits, fitted dynamic programming algorithms, policy gradient methods, imitation learning, and tree search-based planning methods. Our contribution to the RL literature is an approachable and concise presentation of core RL algorithms that balances practical considerations with theoretical rigour. Each chapter includes extensive bibliographic notes that survey recent advances. We hope this textbook will equip the reader with a workable mental model for navigating modern RL research.

Level thesis:degree_level

Bachelor's

Grantor

Harvard University Engineering and Applied Sciences

Year dc:date.issued

2025

Author dc:creator

• Cai, Alexander Dazhen

Advisor dc:contributor.advisor

• Janson, Lucas

Committee members dc:contributor.committeemember

• Janson, Lucas
• Kakade, Sham M

• artificial intelligence
• machine learning
• Markov decision process
• multi-armed bandits
• optimal control
• reinforcement learning
• Statistics
• Computer science

Language dc:language.iso

en

Dc Identifier Other

31931965

OAI identifier oai:identifier

oai:dash.harvard.edu:1/42719543