Preference-Based Reinforcement Learning: A Preliminary Survey

By Christian Wirth, Johannes Fürnkranz. 2013

30 citations recorded by the source corpus.

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In Reinforcement Learning

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).”
Reward learning from human preferences and demonstrations in Atari (2018, background): “…learning from human ratings or rankings (Wirth et al., 2017): Knox and Stone (2009), Pilarski et al. (2011), Akrour et al. (2012), Wilson et al. (2012), Wirth and Fürnkranz (2013), Daniel et al. (2015), El Asri et al. (2016), Wirth et al. (2016), Mathewson and Pilarski (2017), and others.”
Dueling RL: Reinforcement Learning with Trajectory Preferences (2021, methodology): “The choice of reward function in applications such as robotics consequently entails a high amount of non-trivial effort in reward engineering, leading to challenges such as reward shaping, reward hacking, infinite rewards, and multi-objective outcomes (Wirth and Fürnkranz, 2013; Wirth et al., 2017).”
Learning Dynamic Robot-to-Human Object Handover from Human Feedback (2016, background): “Preference-based reinforcement learning algorithms generally do not use absolute reward feedback and rely solely on preference feedback [28].”
Model-Free Preference-Based Reinforcement Learning (2016, background): “Many PBRL approaches rely on a model of the system dynamics which they can use for directed exploration of the utility function.”
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training (2021, background): “…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; Biyik & Sadigh, 2018; Leike et al., 2018; Biyik et al., 2020).”
A Survey of Preference-Based Reinforcement Learning Methods (2017, background): “This paper is based on a preliminary survey (Wirth and Fürnkranz, 2013c), but has been extended in almost every aspect.”
Active Reinforcement Learning: Observing Rewards at a Cost (2020, methodology): “, 2015) and preference-based reinforcement learning (Wirth and Fürnkranz, 2013).”