CS 5180: Reinforcement Learning and Sequential Decision Making
Lecture - 4 credits
- Introduces reinforcement learning and the underlying computational frameworks and the Markov decision process framework.
- Covers a variety of reinforcement learning algorithms, including model-based, model-free, value function, policy gradient, actor-critic, and Monte Carlo methods.
- Examines commonly used representations including deep learning representations and approaches to partially observable problems.
- Students are expected to have a working knowledge of probability and linear algebra, to complete programming assignments, and to complete a course project that applies some form of reinforcement learning to a problem of interest.
Introduces reinforcement learning and the underlying computational frameworks and the Markov decision process framework. Show more.