CS 4180: Reinforcement Learning
Lecture - 4 credits
- Introduces reinforcement learning and the Markov decision process (MDP) framework.
- Covers methods for planning and learning in MDPs such as dynamic programming, model-based methods, and model-free methods.
- Examines commonly used representations including deep-learning representations.
- Students are expected to have a working knowledge of probability, 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 Markov decision process (MDP) framework. Show more.