Recent Advances in Hierarchical Reinforcement Learning
By Andrew Barto, Sridhar Mahadevan. 2003
580 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Reinforcement Learning
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
- Pieter Abbeel
- Chelsea Finn
- Lihong Li
- Peter Stone
- J. Urgen Schmidhuber
- George Dimitri Konidaris
- Jan Peters
In Reinforcement Learning
- Sergey Levine
- Pieter Abbeel
- Peter Stone
- Chelsea Finn
- Lihong Li
- George Dimitri Konidaris
- J. Urgen Schmidhuber
- Shimon Whiteson
Citations in the corpus
- Deep learning in neural networks: An overview (2014, neutral): “Numerous alternative HRL techniques have been proposed (e.g., Bakker & Schmidhuber, 2004; Barto & Mahadevan, 2003; Dietterich, 2000b; Doya, Samejima, Katagiri, & Kawato, 2002; Ghavamzadeh & Mahadevan, 2003; Jameson, 1991; Menache, Mannor, & Shimkin, 2002; Moore & Atkeson, 1995; Precup, Sutton, &…”
- Reinforcement learning in robotics: A survey (2013, background): “It offers a larger set of applicable tools ranging from adaptive discretizations (Buşoniu et al., 2010) and function approximation approaches (Sutton and Barto, 1998) to macro-actions or options (Barto and Mahadevan, 2003; Hart and Grupen, 2011).”
- EX2: Exploration with Exemplar Models for Deep Reinforcement Learning (2017, methodology): “Hierarchical reinforcement learning has traditionally tried to exploit temporal abstraction (Barto & Mahadevan, 2003) and relied on semiMarkov decision processes.”
- Hierarchical multi-agent reinforcement learning (2001, methodology): “Hierarchical methods constitute a general framework for scaling RL to large domains by using the task structure to restrict the space of policies [3].”
- Toward Good Abstractions for Lifelong Learning (2017, result): “The results support the (well established) conclusion that effective hierarchies reduce learning difficulty [9, 14, 3, 2, 7, 16].”
- Hierarchical Reinforcement Learning: A Survey (2015, background): “RL algorithms work on maximizing the agent learning while interacting with its environment directly [17].”
- Intrinsically Motivated Reinforcement Learning (2004, background): “Option models allow stochastic planning methods to be extended to handle planning at higher levels of abstraction.”