Deep Reinforcement Learning
Time and place:
Registration with topic request by e-mail before start of the class; Assignment of presentation topics is FCFS.
- Time and place on appointment
Fields of study
- WPF INF-MA from SEM 2
- WPF CE-MA-SEM from SEM 2
Prerequisites / Organizational information
Registration via e-mail to firstname.lastname@example.org
- Presentation (30-40 minutes)
- Preparation of a report that includes the main points of the talk (not a simply copy of the slides)
- Attending the presentations of other students
- Completion of the slides one week before the talk; completion of the report until the end of the semester
Reinforcement Learning (RL) is a kind of learning that allows an autonomous agent to learn in an environment through a trial-and-error process. In Reinforcement Learning the agent takes actions and observes the environmental feedback. If actions lead to better situations, there is the tendency of applying such behavior again, otherwise, the tendency is to avoid such behavior in the future. Hence, the central problem lies within the optimization of selecting optimal actions in any situation to reach a given goal. In this seminar, students will investigate the key aspects and methods used in nowadays deep reinforcement learning algorithms.
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Expected participants: 10