We provide a theoretical framework for Reinforcement Learning with Human Feedback
(RLHF). We show that when the underlying true reward is linear, under both Bradley-Terry …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Abstract Reinforcement learning from Human Feedback (RLHF) learns from preference
signals, while standard Reinforcement Learning (RL) directly learns from reward signals …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

People often ask others for restaurant recommendations as a way to discover new dining
experiences. This makes restaurant recommendation an exciting scenario for recommender …

We consider the problem of preference-based reinforcement learning (PbRL), where, unlike
traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit …

We consider the problem of preference based reinforcement learning (PbRL), where, unlike
traditional reinforcement learning, an agent receives feedback only in terms of a 1 bit (0/1) …

We derive an algorithm that achieves the optimal (up to constants) pseudo-regret in both
adversarial and stochastic multi-armed bandits without prior knowledge of the regime and …

We derive an algorithm that achieves the optimal (within constants) pseudo-regret in both
adversarial and stochastic multi-armed bandits without prior knowledge of the regime and …

In machine learning, the notion of multi-armed bandits refers to a class of online learning
problems, in which an agent is supposed to simultaneously explore and exploit a given set …