153 citations · 860 across the 45 of their papers we have counts for
7 papers · 1 filter
Nash Learning from Human Feedback
Rémi Munos, Michal Valko, Daniele Calandriello +14
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human preferences. Typically, RLHF involves the in…
A General Theoretical Paradigm to Understand Learning from Human Preferences
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot +4
The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preference…
Bootstrapped Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Mark Rowland +4
In reinforcement learning (RL), state representations are key to dealing with large or continuous state spaces. While one of the promises of deep learning algorithms is to automati…
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm
Yunhao Tang, Tadashi Kozuno, Mark Rowland +4
Multi-step learning applies lookahead over multiple time steps and has proved valuable in policy evaluation settings. However, in the optimal control case, the impact of multi-step…
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
Mark Rowland, Yunhao Tang, Clare Lyle +3
We study the problem of temporal-difference-based policy evaluation in reinforcement learning. In particular, we analyse the use of a distributional reinforcement learning algorith…
VA-learning as a more efficient alternative to Q-learning
Yunhao Tang, Rémi Munos, Mark Rowland +1
In reinforcement learning, the advantage function is critical for policy improvement, but is often extracted from a learned Q-function. A natural question is: Why not learn the adv…