Reinforcement Learning and Dynamic Programming (2022)

Description:
An intelligent system is expected to generate policies autonomously in order to achieve a goal, which is mostly to maximize a given reward function or minimize a given cost function.  Reinforcement learning is a set of methods in machine learning that can produce such policies. In order to learn optimal actions in an environment that is not fully comprehensible to itself, an intelligent system can use reinforcement algorithms to leverage its experience to figure out optimal policies. Nowadays, reinforcement learning techniques are successfully applied in various engineering fields, including robotics (DeepMind’s walking robot) and computers playing games (AlphaGo and TD-Gammon). Developed independently from reinforcement learning, dynamic programming is a set of algorithms in optimal control theory that generate policies assuming that the environment is fully comprehensible to the intelligent system. Therefore, dynamic programming provides an essential base to learn reinforcement learning. The course aims at building a fundamental understanding of both methods based on their intimate relations to each other and on their applications to similar problems.

The course consists of the following topics: Markov decision processes, dynamic programming for infinite time and stopping-time, reinforcement learning, and its convergence proofs.

Organizer: Rafal Wisniewski

Lecturers: Rafal Wisniewski, Zheng-Hua Tan

ECTS: 2.0

Time: 3 - 7 October 2022, from 9 to 12 and 13 to 15 every day

Place: TBA

Number of seats: 40

Deadline: 12 September 2022

Important information concerning PhD courses:
We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.