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

Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks
in which the agent has only limited environmental feedback. Despite many advances over …

General-purpose robotic systems must master a large repertoire of diverse skills to be useful
in a range of daily tasks. While reinforcement learning provides a powerful framework for …

Abstract We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the
context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors-from …

Transfer learning is the improvement of learning in a new task through the transfer of
knowledge from a related task that has already been learned. While most machine learning …

This chapter surveys recent lines of work that use Bayesian techniques for reinforcement
learning. In Bayesian learning, uncertainty is expressed by a prior distribution over unknown …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Transfer in reinforcement learning is a novel research area that focuses on the development
of methods to transfer knowledge from a set of source tasks to a target task. Whenever the …

We study the benefit of sharing representations among tasks to enable the effective use of
deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption …

Reinforcement learning is a major tool to realize intelligent agents that can be autonomously
adaptive to the environment. With deep models, reinforcement learning has shown great …