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Found 7 papers, 1 papers with code
Intelligent problem-solving as integrated hierarchical reinforcement learning
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms.
Hierarchical Reinforcement Learning
reinforcement-learning
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Hierarchical principles of embodied reinforcement learning: A review
We then relate these insights with contemporary hierarchical reinforcement learning methods, and identify the key machine intelligence approaches that realise these mechanisms.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Sensorimotor representation learning for an "active self" in robots: A model survey
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 challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations.
Robotic self-representation improves manipulation skills and transfer learning
Cognitive science suggests that the self-representation is critical for learning and problem-solving.
Reinforcement Learning with Time-dependent Goals for Robotic Musicians
Reinforcement learning is a promising method to accomplish robotic control tasks.
Curious Hierarchical Actor-Critic Reinforcement Learning
Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity.
From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving
In this article, we build on these novel methods to facilitate the integration of action planning with reinforcement learning by exploiting the reward-sparsity as a bridge between the high-level and low-level state- and control spaces.
