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oai:dash.harvard.edu:1/427195432025-12-11T15:22:14Zcom_1_1col_1_4927603

Janson, Lucas Cai, Alexander Dazhen Janson, Lucas Kakade, Sham M 2025-09-18T04:32:32Z 2025 2025-05-16 2025 2025-09-18T04:32:30Z Cai, Alexander Dazhen. 2025. An Introduction to Reinforcement Learning. Bachelors Thesis, Harvard University Engineering and Applied Sciences. 31931965 https://dash.harvard.edu/handle/1/42719543 0000-0003-1308-6352 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. Computer Science application/pdf en artificial intelligence machine learning Markov decision process multi-armed bandits optimal control reinforcement learning Artificial intelligence Statistics Computer science An Introduction to Reinforcement Learning Thesis or Dissertation text LAA Cai, Alexander Dazhen alexandercai@outlook.com 2025 Harvard University Engineering and Applied Sciences Bachelor's Undergraduate Computer Science Publication LAAopen.access