University of California, Los Angeles 4 credits Statistics
Lecture, three hours. Requisite: course 200A. Designed for graduate students. Reinforcement learning (RL) provides the key to enabling machines to make intelligent decisions, learn from their experiences, and optimize their actions in dynamic and uncertain environments. From recommendation systems tailored to individual preferences to self-driving cars navigating complex traffic scenarios, RL is at the heart of enabling these remarkable feats. Study delves into the depth and breadth of RL, covering a wide range of topics from theoretical foundations to practical algorithmic implementations including multi-armed bandits, Markov decision process, and stochastic optimal control. S/U or letter grading.
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