Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …

Reinforcement learning competitions advance the field by providing appropriate scope and
support to develop solutions toward a specific problem. To promote the development of …

Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

Existing approaches for improving generalization in deep reinforcement learning (RL) have
mostly focused on representation learning, neglecting RL-specific aspects such as …

The limited priors required by neural networks make them the dominating choice to encode
and learn policies using reinforcement learning (RL). However, they are also black-boxes …

We provide a framework to accelerate reinforcement learning (RL) algorithms by heuristics
that are constructed by domain knowledge or offline data. Tabula rasa RL algorithms require …

Radio access network (RAN) technologies continue to evolve, with Open RAN gaining the
most recent momentum. In the O-RAN specifications, the RAN intelligent controllers (RICs) …

One of the key promises of model-based reinforcement learning is the ability to generalize
using an internal model of the world to make predictions in novel environments and tasks …

We propose a new class of deep reinforcement learning (RL) algorithms that model latent
representations in hyperbolic space. Sequential decision-making requires reasoning about …

A highly desirable property of a reinforcement learning (RL) agent--and a major difficulty for
deep RL approaches--is the ability to generalize policies learned on a few tasks over a high …