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

Preference-based reinforcement learning (RL) has shown potential for teaching agents to
perform the target tasks without a costly, pre-defined reward function by learning the reward …

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) 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 …

Reinforcement learning (RL) requires access to a reward function that incentivizes the right
behavior, but these are notoriously hard to specify for complex tasks. Preference-based RL …

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

The complexity of designing reward functions has been a major obstacle to the wide
application of deep reinforcement learning (RL) techniques. Describing an agent's desired …

The current reward learning from human preferences could be used to resolve complex
reinforcement learning (RL) tasks without access to a reward function by defining a single …

While reinforcement learning (RL) has become a more popular approach for robotics,
designing sufficiently informative reward functions for complex tasks has proven to be …