Preference-learning based Inverse Reinforcement Learning for Dialog Control

By Hiroaki Sugiyama, Toyomi Meguro, Yasuhiro Minami. 2012

44 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

1. Sergey Levine
2. Jianfeng Gao
3. Scott Niekum
4. Pieter Abbeel
5. Anca Dragan
6. Milica Gasic
7. Richard Socher
8. Percy Liang

In Reinforcement Learning

1. Sergey Levine
2. Scott Niekum
3. Anca Dragan
4. Pieter Abbeel
5. Daniel S. Brown
6. Jan Leike
7. Shane Legg
8. Gerhard Neumann

Citations in the corpus

• Deep Reinforcement Learning from Human Preferences (2017, background): “…reinforcement learning from human ratings or rankings, including Akrour et al. (2011), Pilarski et al. (2011), Akrour et al. (2012), Wilson et al. (2012), Sugiyama et al. (2012), Wirth and Fürnkranz (2013), Daniel et al. (2015), El Asri et al. (2016), Wang et al. (2016), and Wirth et al. (2016).”
• Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations (2019, background): “Our work can be considered a form of preference-based IRL (PBIRL) (Wirth et al., 2016; Sugiyama et al., 2012) which is an extension the IRL framework that considers a set of demonstrations with preference labels.”
• A Survey of Preference-Based Reinforcement Learning Methods (2017, background): “Other authors (Fürnkranz et al., 2012; Wirth and Fürnkranz, 2012, 2013a,b, 2015; Runarsson and Lucas, 2012, 2014; Sugiyama et al., 2012) assume a utility function directly defining the expected outcome of a state or action.”
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training (2021, background): “Preference-based learning is thus an attractive alternative because the supervision is easy to provide yet information-rich (Akrour et al., 2011; Pilarski et al., 2011; Akrour et al., 2012; Wilson et al., 2012; Sugiyama et al., 2012; Wirth & Fürnkranz, 2013; Wirth et al., 2016; Sadigh et al., 2017; ”
• Active Preference-Based Learning of Reward Functions (2017, background): “Typically in preference-based learning, the queries are actively selected by searching some discrete or sampled set (e.g., [3, 9, 12, 13, 20, 21]).”
• On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems (2016, background): “In (Sugiyama et al., 2012), users were asked to provide rankings between pairs of dialogues.”