activity
20182026
most citedDisentangled Skill Embeddings for Reinforcement Learning

9 citations · 23 across the 9 of their papers we have counts for

collaborators

14 papers

cs.MA2026

A Generative Model of Conspicuous Consumption and Status Signaling

Logan Cross, Jordi Grau-Moya, William A. Cunningham +2

Status signaling drives human behavior and the allocation of scarce resources such as mating opportunities, yet the generative mechanisms governing how specific goods, signals, or…

cs.AI2025

Code World Models for General Game Playing

Wolfgang Lehrach, Daniel Hennes, Miguel Lazaro-Gredilla +13

Large Language Models (LLMs) reasoning abilities are increasingly being applied to classical board and card games, but the dominant approach -- involving prompting for direct move…

cs.LG2025

Understanding Prompt Tuning and In-Context Learning via Meta-Learning

Tim Genewein, Li Kevin Wenliang, Jordi Grau-Moya +3

Prompting is one of the main ways to adapt a pretrained model to target tasks. Besides manually constructing prompts, many prompt optimization methods have been proposed in the lit…

cs.LG2025

LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities

Thomas Schmied, Jörg Bornschein, Jordi Grau-Moya +2

The success of Large Language Models (LLMs) has sparked interest in various agentic applications. A key hypothesis is that LLMs, leveraging common sense and Chain-of-Thought (CoT)…

cs.LG2025

Partition Tree Weighting for Non-Stationary Stochastic Bandits

Joel Veness, Marcus Hutter, Andras Gyorgy +1

This paper considers a generalisation of universal source coding for interaction data, namely data streams that have actions interleaved with observations. Our goal will be to cons…

cs.AI2022

Beyond Bayes-optimality: meta-learning what you know you don't know

Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch +11

Meta-training agents with memory has been shown to culminate in Bayes-optimal agents, which casts Bayes-optimality as the implicit solution to a numerical optimization problem rath…