Preference-based reinforcement learning (PbRL) is an approach that enables RL agents to
learn from preference, which is particularly useful when formulating a reward function is …

Reinforcement Learning (RL) algorithms suffer from the dependency on accurately
engineered reward functions to properly guide the learning agents to do the required tasks …

Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a
suitably chosen reward function. However, designing such a reward function often requires …

Reward functions are difficult to design and often hard to align with human intent. Preference-
based Reinforcement Learning (RL) algorithms address these problems by learning reward …

Preference-based reinforcement learning (PbRL) provides a natural way to align RL agents'
behavior with human desired outcomes, but is often restrained by costly human feedback …

Abstract Setting up a well-designed reward function has been challenging for many
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …

Conveying complex objectives to reinforcement learning (RL) agents often requires
meticulous reward engineering. Preference-based RL methods are able to learn a more …

Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent
learns to optimize a task using pair-wise preference-based feedback over trajectories, rather …

This paper makes a first step toward the integration of two subfields of machine learning,
namely preference learning and reinforcement learning (RL). An important motivation for a …

Abstract Preference-based Reinforcement Learning (PbRL) replaces reward values in
traditional reinforcement learning by preferences to better elicit human opinion on the target …