Skill Characterization Based on Betweenness

By Özgür Simsek, Andrew Barto. 2008

186 citations recorded by the source corpus.

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “Numerous alternative HRL techniques have been proposed (e.g., Ring, 1991, 1994; Jameson, 1991; Tenenberg et al., 1993; Moore and Atkeson, 1995; Precup et al., 1998; Dietterich, 2000b; Menache et al., 2002; Doya et al., 2002; Ghavamzadeh and Mahadevan, 2003; Barto and Mahadevan, 2003; Samejima et al.”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “Numerous alternative HRL techniques have been proposed [204, 206, 117, 279, 295, 171, 195, 50, 162, 51, 15, 215, 11, 260].”
Time-Agnostic Prediction: Predicting Predictable Video Frames (2018, background): “Most approaches (S¸ims¸ek & Barto, 2009; Bacon, 2013; Metzen, 2013) construct full transition graphs and apply notions of graph centrality to locate bottlenecks.”
Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining (2009, methodology): “In previous research, option goal states have been selected by a variety of methods, the most common relying on computing visit or reward statistics over individual states to identify useful subgoals [5, 6, 7, 8].”
Learning Abstract Options (2018, background): “Extensive research has focused on discovering these temporal abstractions autonomously [21, 40, 22, 39, 38] while approaches that can be used in continuous state and/or action spaces have only recently became feasible [13, 26, 20, 19, 14, 43, 4].”
Value Preserving State-Action Abstractions (2020, background): “Options are known to aid in transfer (Konidaris and Barto, 2007, 2009; Brunskill and Li, 2014; Topin et al., 2015), encourage better exploration (Şimşek and Barto, 2004, 2009; Brunskill and Li, 2014; Bacon et al., 2017; Fruit and Lazaric, 2017; Machado et al., 2017; Tiwari and Thomas, 2019;…”
Discovering Options for Exploration by Minimizing Cover Time (2019, methodology): “S¸ims¸ek & Barto (2009) generalized the concept of bottleneck states to the (shortest-path) betweenness of the graph to capture how pivotal the state is.”