Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

In many real-world applications, reinforcement learning (RL) agents might have to solve
multiple tasks, each one typically modeled via a reward function. If reward functions are …

Abstract Purpose of Review Research in underwater manipulation has mostly focused on
solving individual parts of the manipulation challenge; however, we believe a systemic …

Broad-XAI moves away from interpreting individual decisions based on a single datum and
aims to provide integrated explanations from multiple machine learning algorithms into a …

This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a
variant of conventional reinforcement learning that dispenses with the need for online …

In popular media, there is often a connection drawn between the advent of awareness in
artificial agents and those same agents simultaneously achieving human or superhuman …

Reinforcement Learning (RL) seeks to develop systems capable of autonomous decision-
making by learning through interaction with their environment. Central to this process are …

In this paper, we study the expressivity of scalar, Markovian reward functions in
Reinforcement Learning (RL), and identify several limitations to what they can express …

We introduce MO-Gym, an extensible library containing a diverse set of multi-objective
reinforcement learning environments. It introduces a standardized API that facilitates …