Reinforcement Learning (RL) agents often learn policies that do not generalise across tasks in which the environmental features and optimal skills are different [des Combes et al., 2018, Garcin et al., 2024].

When artificial agents are jointly trained to perform collaborative tasks using a communication channel, they develop opaque goal-oriented communication protocols.

A well-known dilemma in large vision-language models ( e.g., GPT -4, LLaV A) is that while increasing the number of vision tokens generally enhances visual

With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training stability.

Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps.

In this paper, we formalize the active set ordering problem, which involves actively discovering a set of inputs based on their orderings determined by expensive evaluations of a blackbox function.

Existing VLN models are optimized through expert demonstrations by supervised behavioural cloning or incorporating manual reward engineering.

Offline reinforcement learning (RL) aims to learn a policy from a fixed dataset without additional interactions with the environment.