Preference-based Evolutionary Direct Policy Search

By Róbert Istvan Busa-Fekete, Balázs Szörényi, Paul Weng, Weiwei Cheng, Eyke Hüllermeier. 2013

22 citations recorded by the source corpus.

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Citations in the corpus

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].”
Preference-Based Reinforcement Learning: A Preliminary Survey (2013, unknown): “[7] is closely related to the work of Akrour et al.”
Reward-rational (implicit) choice: A unifying formalism for reward learning (2020, background): “Most work on comparisons is done in the preference-based RL domain in which the robot might compute a policy directly to agree with the comparisons, rather than explicitly recover the reward function [31, 32].”
Dueling Posterior Sampling for Preference-Based Reinforcement Learning (2019, background): “Existing approaches for trajectory-level preferencebased RL may be broadly divided into three categories (Wirth, 2017): a) directly optimizing policy parameters (Wilson et al., 2012; Busa-Fekete et al., 2013; Kupcsik et al., 2018); b) modeling action preferences in each state (Fürnkranz et al.”
A General Theoretical Paradigm to Understand Learning from Human Preferences (2023, background): “Notable exceptions, that consider specific special cases, are (Wang et al., 2023; Chen et al., 2022) and prior work on preference-based (Busa-Fekete et al., 2014, 2013) and dueling bandits and RL (Novoseller et al., 2020; Pacchiano et al., 2023).”
A Survey of Preference-Based Reinforcement Learning Methods (2017, methodology): “Busa-Fekete et al. (2013, 2014) realized that idea, which we discuss in Sec.”