2 citations · 3 across the 9 of their papers we have counts for
10 papers
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
Anthony GX-Chen, Ankit Anand, Gheorghe Comanici +7
Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward. Yet, modern applications such as language model fin…
Plasticity as the Mirror of Empowerment
David Abel, Michael Bowling, André Barreto +13
Agents are minimally entities that are influenced by their past observations and act to influence future observations. This latter capacity is captured by empowerment, which has se…
Agency Is Frame-Dependent
David Abel, André Barreto, Michael Bowling +13
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determini…
Optimizing Return Distributions with Distributional Dynamic Programming
Bernardo Ávila Pires, Mark Rowland, Diana Borsa +6
We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standard reinforcement learning as a special ca…
Foundations of Multivariate Distributional Reinforcement Learning
Harley Wiltzer, Jesse Farebrother, Arthur Gretton +1
In reinforcement learning (RL), the consideration of multivariate reward signals has led to fundamental advancements in multi-objective decision-making, transfer learning, and repr…
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning
Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires +7
Learning a good representation is a crucial challenge for Reinforcement Learning (RL) agents. Self-predictive learning provides means to jointly learn a latent representation and d…
Human Alignment of Large Language Models through Online Preference Optimisation
Daniele Calandriello, Daniel Guo, Remi Munos +10
Ensuring alignment of language models' outputs with human preferences is critical to guarantee a useful, safe, and pleasant user experience. Thus, human alignment has been extensiv…
A Distributional Analogue to the Successor Representation
Harley Wiltzer, Jesse Farebrother, Arthur Gretton +5
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process. Anal…
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
Mark Rowland, Li Kevin Wenliang, Rémi Munos +3
We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative…
Distributional Bellman Operators over Mean Embeddings
Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison +4
We propose a novel algorithmic framework for distributional reinforcement learning, based on learning finite-dimensional mean embeddings of return distributions. We derive several…