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NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information. | |
Spring 2023 Op Research - Computer Science E4529 section 001 Reinforcement Learning | |
| Call Number | 15218 |
| Day & Time Location |
TR 5:40pm-6:55pm 524 Seeley W. Mudd Building |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Farid Alizadeh |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | Markov Decision Processes (MDP) and Reinforcement Learning (RL) problems. Reinforcement Learning algorithms including Q-learning, policy gradient methods, actor-critic method. Reinforcement learning while doing exploration-exploitation dilemma, multi-armed bandit problem. Monte Carlo Tree Search methods, Distributional, Multi-agent, and Causal Reinforcement Learning. |
| Web Site | Vergil |
| Department | Industrial Engineering and Operations Research |
| Enrollment | 26 students (55 max) as of 9:06PM Thursday, March 23, 2023 |
| Subject | Op Research - Computer Science |
| Number | E4529 |
| Section | 001 |
| Division | School of Engineering and Applied Science: Graduate |
| Campus | Morningside |
| Section key | 20231ORCS4529E001 |
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