Programming by Feedback

By Marc Schoenauer, Riad Akrour, Michèle Sebag, Jean-Christophe Souplet. 2014

72 citations recorded by the source corpus.

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In Reinforcement Learning

Citations in the corpus

Deep Reinforcement Learning from Human Preferences (2017, methodology): “One notable difference is that Akrour et al. (2012) and Akrour et al. (2014) elicit preferences over whole trajectories rather than short clips.”
Scalable agent alignment via reward modeling: a research direction (2018, background): “…Asri et al., 2016), actions (Grifﬁth et al., 2013), value (Knox & Stone, 2009), advantage (MacGlashan et al., 2017), or preferences over trajectories (Fürnkranz et al., 2012; Akrour et al., 2012, 2014; Wirth et al., 2017); and providing an explicit objective function (Hadﬁeld-Menell et al., 2017b).”
Leveraging Human Guidance for Deep Reinforcement Learning Tasks (2019, methodology): “Previous works have used preferences to directly learn policies [Wilson et al. , 2012; Busa-Fekete et al. , 2013], learn a preference model [F¨urnkranz et al. , 2012], or learn a reward function [Wirth et al. , 2016; Akrour et al. , 2014].”
On the Theory of Reinforcement Learning with Once-per-Episode Feedback (2021, background): “Another closely related line of work studies reinforcement learning with preference feedback [2, 15, 3, 5, 37, 26, 38].”
Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019, background): “Preferences can be elicited with whole episode comparisons [2, 3, 14, 18] or shorter clip comparisons [15, 29].”
Dueling Posterior Sampling for Preference-Based Reinforcement Learning (2019, methodology): “[4] Riad Akrour, Marc Schoenauer, Michèle Sebag, and Jean-Christophe Souplet.”
Active reward learning with a novel acquisition function (2015, background): “Schoenauer et al. (2014) show how a preference based learning algorithm can be used in the cart-pole balancing task with continuous states and discrete actions.”