This course covers reinforcement learning aka dynamic programming, which is a modeling principle capturing dynamic environments and stochastic nature of events. The main goal is to learn dynamic ...

We discuss the dynamic programming approach to finding an optimal sequence of a set of tasks when the tasks are related by precedence restrictions. We describe how to use this approach in problems ...

It is proved that there exist stationary optimal plans for discounted dynamic programming problems, and that there exist semi-Markov ∈-optimal plans for positive dynamic programming problems. The ...

It covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) .