In recent years, the development of robotics and artificial intelligence (AI) systems has been
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …

In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …

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

In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …

In many sequential decision-making problems (eg, robotics control, game playing,
sequential prediction), human or expert data is available containing useful information about …

Deep reinforcement learning (RL) has led to many recent and groundbreaking advances.
However, these advances have often come at the cost of both increased scale in the …

The combination of deep neural network models and reinforcement learning algorithms can
make it possible to learn policies for robotic behaviors that directly read in raw sensory …

Learning to imitate expert behavior from demonstrations can be challenging, especially in
environments with high-dimensional, continuous observations and unknown dynamics …

Abstract Designing rewards for Reinforcement Learning (RL) is challenging because it
needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter …

Learning socially-aware motion representations is at the core of recent advances in multi-
agent problems, such as human motion forecasting and robot navigation in crowds. Despite …