EFFICIENT ABSTRACTION SELECTION IN REINFORCEMENT LEARNING
By Harm van Seijen, Shimon Whiteson, L. Kester. 2013
17 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Scott Niekum
- George Dimitri Konidaris
- Natalia Díaz Rodríguez
- David Filliat
- Timothée Lesort
- Oliver Kroemer
- Pieter Abbeel
In Management Science and Operations Research
- Scott Niekum
- George Dimitri Konidaris
- Oliver Kroemer
- Alex Kulesza
- Satinder Singh
- Oliver Brock
- David Filliat
- Natalia Díaz Rodríguez
Citations in the corpus
- Unsupervised state representation learning with robotic priors: a robustness benchmark (2017, methodology): “This is an alternative approach to selecting a state representation from a set ([7], [8]), or creating an autoencoder to compress information into a lower dimensional state ([9], [10]), [11].”
- Abstraction Selection in Model-based Reinforcement Learning (2015, background): “For recent empirical advances on the problem, we refer the reader to Konidaris & Barto (2009), Cobo et al. (2014), and Seijen et al. (2014).”
- Learning state representations with robotic priors (2015, background): “State representation learning is related to the problem of abstraction selection in hierarchical reinforcement learning, which deals with selecting a state representation from a given set [35, 17] or choosing a subset of state dimensions from a given representation [4].”
- A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, background): “Each skill can use its own internal skill-specific abstraction [90, 201, 400, 69, 178] that allows it to focus on only relevant objects and state features, decomposing a problem that”
- HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators (2019, unknown): “While most of the previous work on abstraction selection refers to state abstraction [40] (mapping primitive state space S to an abstract state space h(S)), our work performs abstraction selection in the action space.”