Reinforcement Learning (cancelled in summer term 2021, moved to winter term)
- type: Lecture / Practice (VÜ)
- semester: SS 2021
- lecturer: Prof. Dr. Gerhard Neumann
- sws: SWS: 3 / ECTS: 5
- lv-no.: 2400163
- information: Online
Content | Reinforcement Learning (RL) is a sub-field of machine learning in which an artificial agent has to interact with its environment and learn how to improve its behaviour by trial and error. For doing so, the agent is provided with an evaluative feedback signal, called reward, that he perceives for each action performed in its environment. RL is one of the hardest machine learning problems, as, in contrast to standard supervised learning, we do not know the targets (i.e. the optimal actions) for our inputs (i.e. the state of the environment) and we also need to consider the long-term effects of the agent’s actions on the state of the environment. Due to recent successes, RL has gained a lot of popularity with applications in robotics, automation, health care, trading and finance, natural language processing, autonomous driving and computer games. This lecture will introduce the concepts and theory of RL and review current state of the art methods with a particular focus on RL applications in robotics. An exemplary list of topics is given below:
Lernziele: - Students are able to understand the RL problem and challenges. - Students can differentiate between different RL algorithm and understand their underlying theory - Students will know the mathematical tools necessary to understand RL algorithms - Students can implement RL algorithms for various tasks - Students understand current research questions in RL Empfehlungen:
Erfolgskontrolle: Siehe Modulhandbuch!
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Language of instruction | English |
Organisational issues | Soll erst im WS 2021 stattfinden, als Blockvorlesung, voraussichtlich in den letzten 2 Oktober Wochen
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