As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

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

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Generalization is a central challenge for the deployment of reinforcement learning (RL)
systems in the real world. In this paper, we show that the sequential structure of the RL …

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …

Deep reinforcement learning has achieved many impressive results in recent years.
However, tasks with sparse rewards or long horizons continue to pose significant …

Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

Movement Primitives (MP) are a well-established approach for representing modular and re-
usable robot movement generators. Many state-of-the-art robot learning successes are …

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 …

Movement Primitives are a well-established paradigm for modular movement representation
and generation. They provide a data-driven representation of movements and support …