Autonomous robots facing a diversity of open environments and performing a variety of tasks
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …

Large language models (LLMs)(Anil et al., 2023; Glaese et al., 2022; OpenAI, 2023; Ouyang
et al., 2022) have made remarkable strides in enhancing natural language understanding …

For sophisticated reinforcement learning (RL) systems to interact usefully with real-world
environments, we need to communicate complex goals to these systems. In this work, we …

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

To solve complex real-world problems with reinforcement learning, we cannot rely on
manually specified reward functions. Instead, we need humans to communicate an objective …

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 …

Abstract Machine learning (ML) is the fastest growing field in computer science, and health
informatics is among the greatest challenges. The goal of ML is to develop algorithms which …

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

Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular
paradigm for aligning models with human intent. Typically RLHF algorithms operate in two …

Reward is the driving force for reinforcement-learning agents. This paper is dedicated to
understanding the expressivity of reward as a way to capture tasks that we would want an …