93 citations · 95 across the 4 of their papers we have counts for
8 papers
Intelligent problem-solving as integrated hierarchical reinforcement learning
Manfred Eppe, Christian Gumbsch, Matthias Kerzel +3
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. H…
Hierarchical principles of embodied reinforcement learning: A review
Manfred Eppe, Christian Gumbsch, Matthias Kerzel +3
Cognitive Psychology and related disciplines have identified several critical mechanisms that enable intelligent biological agents to learn to solve complex problems. There exists…
Robotic self-representation improves manipulation skills and transfer learning
Phuong D. H. Nguyen, Manfred Eppe, Stefan Wermter
Cognitive science suggests that the self-representation is critical for learning and problem-solving. However, there is a lack of computational methods that relate this claim to co…
Reinforcement Learning with Time-dependent Goals for Robotic Musicians
Thilo Fryen, Manfred Eppe, Phuong D. H. Nguyen +2
Reinforcement learning is a promising method to accomplish robotic control tasks. The task of playing musical instruments is, however, largely unexplored because it involves the ch…
Sensorimotor representation learning for an "active self" in robots: A model survey
Phuong D. H. Nguyen, Yasmin Kim Georgie, Ezgi Kayhan +3
Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the chal…
Curious Hierarchical Actor-Critic Reinforcement Learning
Frank Röder, Manfred Eppe, Phuong D. H. Nguyen +1
Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of…