The past decade has witnessed the rapid development and adoption of machine and deep
learning (ML & DL) methodologies in agricultural systems, showcased by great successes in …

We introduce Genie, the first* generative interactive environment* trained in an
unsupervised manner from unlabelled Internet videos. The model can be prompted to …

Reinforcement learning (RL) has become a dominant decision-making paradigm and has
achieved notable success in many real-world applications. Notably, deep neural networks …

Language agents have shown some ability to interact with an external environment, eg, a
virtual world such as ScienceWorld, to perform complex tasks, eg, growing a plant, without …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

With the advancements of artificial intelligence (AI), we're seeing more scenarios that require
AI to work closely with other agents, whose goals and strategies might not be known …

A lot of recent machine learning research papers have``open-ended learning''in their title.
But very few of them attempt to define what they mean when using the term. Even worse …

Computational experiments have emerged as a valuable method for studying complex
systems, involving the algorithmization of counterfactuals. However, accurately representing …

This paper introduces ARCLE, an environment designed to facilitate reinforcement learning
research on the Abstraction and Reasoning Corpus (ARC). Addressing this inductive …

Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become
increasingly important in recent years. A major challenge is task open-endedness. In …