Unsupervised pre-training has recently become the bedrock for computer vision and natural
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …

In off-policy deep reinforcement learning, it is usually hard to collect sufficient successful
experiences with sparse rewards to learn from. Hindsight experience replay (HER) enables …

In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs).
Recent works have shown that attention-based RL models outperform recurrent neural …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

Programs offer compactness and structure that makes them an attractive representation for
visual data. We explore how code rewriting can be used to improve systems for inferring …

Goal-oriented reinforcement learning has recently been a practical framework for robotic
manipulation tasks, in which an agent is required to reach a certain goal defined by a …

Efficient exploration of design spaces is highly sought after in engineering applications. A
spectrum of tools has been proposed to deal with the computational difficulties associated …

The adoption of robotics is promising to improve the efficiency, quality, and safety of
prefabricated construction. Besides technologies that improve the capability of a single …

In active visual tracking, it is notoriously difficult when distracting objects appear, as
distractors often mislead the tracker by occluding the target or bringing a confusing …

Purpose Compared with traditional methods relying on manual teaching or system
modeling, data-driven learning methods, such as deep reinforcement learning and imitation …