In order to mitigate some of the inefficiencies of Reinforcement Learning (RL), modular
approaches composing different decision-making policies to derive agents capable of …

Improving the generalization ability of reinforcement learning (RL) agents is an open and
challenging problem and has gradually received attention in recent years. Previous work …

In modular reinforcement learning (MRL), a complex decision making problem is
decomposed into multiple simpler subproblems each solved by a separate module. Often …

A significant challenge facing researchers in the area of multi-agent reinforcement learning
(MARL) pertains to the identification of a library that can offer fast and compatible …

Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable
breakthroughs in solving large-scale decision-making tasks. Nonetheless, most existing …

Abstract skrl is an open-source modular library for reinforcement learning written in Python
and designed with a focus on readability, simplicity, and transparency of algorithm …

Breakthrough advances in reinforcement learning (RL) research have led to a surge in the
development and application of RL. To support the field and its rapid growth, several …

Deep reinforcement learning has contributed to dramatic advances in many tasks, such as
playing games, controlling robots, and navigating complex environments. However, it …

Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in
reinforcement learning (RL) research. However, RL systems, when applied to large-scale …

Hierarchical architectures are critical to the scalability of reinforcement learning methods.
Most current hierarchical frameworks execute actions serially, with macro-actions …