Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

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 …

Unsupervised reinforcement learning aims to acquire skills without prior goal
representations, where an agent automatically explores an open-ended environment to …

Standard RL algorithms assume fixed environment dynamics and require a significant
amount of interaction to adapt to new environments. We introduce Policy-Dynamics Value …

Abstract Model-based offline reinforcement learning approaches generally rely on bounds of
model error. Estimating these bounds is usually achieved through uncertainty estimation …

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has
yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning …

Deep Reinforcement Learning (DRL) aims to create intelligent agents that can learn to solve
complex problems efficiently in a real-world environment. Typically, two learning goals …

Standard RL algorithms assume fixed environment dynamics and require a significant
amount of interaction to adapt to new environments. We introduce Policy-Dynamics Value …

An important issue in competitive multiagent scenarios is the distribution mismatch between
training and testing caused by variations in other agents' policies. As a result, policies …

Learning adaptable policies in the absence of explicit reward signals is a challenging
problem in reinforcement learning. We propose an algorithm that disentangles the …