Auflistung nach Schlagwort "Deep Reinforcement Learning"
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- KonferenzbeitragAveraging rewards as a first approach towards Interpolated Experience Replay(INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge), 2019) Pilar von Pilchau, WenzelReinforcement learning and especially deep reinforcement learning are research areas which are getting more and more attention. The mathematical method of interpolation is used to get information of data points in an area where only neighboring samples are known and thus seems like a good expansion for the experience replay which is a major component of a variety of deep reinforcement learning methods. Interpolated experiences stored in the experience replay could speed up learning in the early phase and reduce the overall amount of exploration needed. A first approach of averaging rewards in a setting with unstable transition function and very low exploration is implemented and shows promising results that encourage further investigation.
- TextdokumentMultiple Sequence Alignment using Deep Reinforcement Learning(SKILL 2021, 2021) Joeres, RomanMultiple sequence alignment (MSA) is one of the primal problems in biology and bioinformatics. The question of how to align multiple sequences correctly is crucial for many other fields of research, e.g., gaining information about the evolutionary distance of two or more sequences and therefore about their corresponding species, finding protein targets for drugs, or finding a drug for a certain target protein. Reinforcement learning (RL), and especially deep reinforcement learning (DRL), has become popular in recent years. To name just a few, DRL has shown major success in complex games such as Atari Games, Chess, and Go. We model the problem of aligning multiple sequences as a Markov decision process (MDP) and examine the performance of different (D)RL algorithms compared to state-of-the-art tools.