This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Many potential applications of reinforcement learning (RL) require guarantees that the agent
will perform well in the face of disturbances to the dynamics or reward function. In this paper …

Due to advancements in data collection, storage, and processing techniques, machine
learning has become a thriving and dominant paradigm. However, one of its main …

We study the problem of representational transfer in RL, where an agent first pretrains in a
number of\emph {source tasks} to discover a shared representation, which is subsequently …

We focus on the task of approximating the optimal value function in deep reinforcement
learning. This iterative process is comprised of solving a sequence of optimization problems …

In this paper we investigate the properties of representations learned by deep reinforcement
learning systems. Much of the early work on representations for reinforcement learning …

Despite the remarkable progress made in visual reinforcement learning (RL) in recent years,
sample inefficiency remains a major challenge. Many existing approaches attempt to …

Many real-world sequential decision-making problems involve critical systems with financial
risks and human-life risks. While several works in the past have proposed methods that are …

We propose RAPid-Learn (Learning to Recover and Plan Again), a hybrid planning and
learning method, to tackle the problem of adapting to sudden and unexpected changes in an …

Reinforcement learning has a demand for massive data in complex problems, which makes
it infeasible to be applied to real cases where sampling is difficult. The key to coping with …