Gaussian processes (GPs) provide a framework for Bayesian inference that can offer
principled uncertainty estimates for a large range of problems. For example, if we consider …

Volt-VAR control (VVC) is a critical tool to manage voltage profiles and reactive power flow
in power distribution networks by setting voltage regulating and reactive power …

Traditional pain assessment approaches ranging from self-reporting methods, to
observational scales, rely on the ability of an individual to accurately assess and …

Empowerment is an information-theoretic method that can be used to intrinsically motivate
learning agents. It attempts to maximize an agent's control over the environment by …

In reinforcement learning (RL), simplicity is typically quantified on an action-by-action basis--
but this timescale ignores temporal regularities, like repetitions, often present in sequential …

Despite the success of cooperative multi-agent reinforcement learning algorithms, most of
them focus on a single team composition, which prevents them from being used in more …

One notable weakness of current machine learning algorithms is the poor ability of models
to solve new problems without forgetting previously acquired knowledge. The Continual …

We propose a novel framework for multi-task reinforcement learning (MTRL). Using a
variational inference formulation, we learn policies that generalize across both changing …

Reinforcement learning with natural language action space often suffers from the curse of
dimensionality due to the combinatorial nature of the natural language. Previous research …

In a cooperative multiagent system, a collection of agents executes a joint policy in order to
achieve some common objective. The successful deployment of such systems hinges on the …