COM SCI 260R
Reinforcement Learning
Description: Lecture, four hours; discussion, two hours; outside study, six hours. Fundamentals and advanced topics of reinforcement learning (RL), computational learning approach where agent tries to maximize total amount of reward it receives while interacting with complex and uncertain environments. Includes introduction of Markov decision processes, model-free RL and model-based RL methods, policy optimization, RL distributed system design, as well as case studies of RL in game playing such as AlphaGo, traffic simulation, autonomous driving, and other machine autonomy applications. Advanced topics of RL such as multi-agent RL, human-in-loop method, and imitation learning. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2023 - Interesting material, but the lectures were pretty confusing to understand. The homeworks take a while to do just because of the training time, and I didn't they were particularly helpful for understanding the material. I thought the final exam was pretty fair. The last project is like a class-wide competition to see who can train the best model
Fall 2023 - Interesting material, but the lectures were pretty confusing to understand. The homeworks take a while to do just because of the training time, and I didn't they were particularly helpful for understanding the material. I thought the final exam was pretty fair. The last project is like a class-wide competition to see who can train the best model
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