Abstract Recent curriculum Reinforcement Learning (RL) has shown notable progress in
solving complex tasks by proposing sequences of surrogate tasks. However, the previous …

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a
tailored sequence of learning tasks, starting from easy ones and subsequently increasing …

Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth
century and has led to a plethora of methods for answering many theoretical and applied …

Reinforcement Learning (RL) algorithms are often known for sample inefficiency and difficult
generalization. Recently, Unsupervised Environment Design (UED) emerged as a new …

In this work, we first formulate the problem of robotic water scoo** using goal-conditioned
reinforcement learning. This task is particularly challenging due to the complex dynamics of …

Training high-performing large language models (LLMs) from scratch is a notoriously
expensive and difficult task, costing hundreds of millions of dollars in compute alone. These …

Current reinforcement learning (RL) often suffers when solving a challenging exploration
problem where the desired outcomes or high rewards are rarely observed. Even though …

A pervasive challenge in Reinforcement Learning (RL) is the``curse of dimensionality''which
is the exponential growth in the state-action space when optimizing a high-dimensional …

Abstract Curriculum Reinforcement Learning (CRL) is an approach to facilitate the learning
process of agents by structuring tasks in a sequence of increasing complexity. Despite its …

Simulation is an indispensable tool in the development and testing of autonomous vehicles
(AVs), offering an efficient and safe alternative to road testing. An outstanding challenge with …